OnTARi: an ontology for factors influencing therapy adherence to rehabilitation.

BACKGROUND: Adherence and motivation are key factors for successful treatment of patients with chronic diseases, especially in long-term care processes like rehabilitation. However, only a few patients achieve good treatment adherence. The causes are manifold. Adherence-influencing factors vary depending on indications, therapies, and individuals. Positive and negative effects are rarely confirmed or even contradictory. An ontology seems to be convenient to represent existing knowledge in this domain and to make it available for information retrieval. METHODS: First, a manual data extraction of current knowledge in the domain of treatment adherence in rehabilitation was conducted. Data was retrieved from various sources, including basic literature, scientific publications, and health behavior models. Second, all adherence and motivation factors identified were formalized according to the ontology development methodology METHONTOLOGY. This comprises the specification, conceptualization, formalization, and implementation of the ontology "Ontology for factors influencing therapy adherence to rehabilitation" (OnTARi) in Protégé. A taxonomy-oriented evaluation was conducted by two domain experts. RESULTS: OnTARi includes 281 classes implemented in ontology web language, ten object properties, 22 data properties, 1440 logical axioms, 244 individuals, and 1023 annotations. Six higher-level classes are differentiated: (1) Adherence, (2) AdherenceFactors, (3) AdherenceFactorCategory, (4) Rehabilitation, (5) RehabilitationForm, and (6) RehabilitationType. By means of the class AdherenceFactors 227 adherence factors, thereof 49 hard factors, are represented. Each factor involves a proper description, synonyms, possibly existing acronyms, and a German translation. OnTARi illustrates links between adherence factors through 160 influences-relations. Description logic queries implemented in Protégé allow multiple targeted requests, e.g., for the extraction of adherence factors in a specific rehabilitation area. CONCLUSIONS: With OnTARi, a generic reference model was built to represent potential adherence and motivation factors and their interrelations in rehabilitation of patients with chronic diseases. In terms of information retrieval, this formalization can serve as a basis for implementation and adaptation of conventional rehabilitative measures, taking into account (patient-specific) adherence factors. OnTARi also enables the development of medical assistance systems to increase motivation and adherence in rehabilitation processes.

INBED: A Highly Specialized System for Bed-Exit-Detection and Fall Prevention on a Geriatric Ward.

OBJECTIVE: In geriatric institutions, the risk of falling of patients is very high and frequently leads to fractures of the femoral neck, which can result in serious consequences and medical costs. With regard to the current numbers of elderly people, the need for smart solutions for the prevention of falls in clinical environments as well as in everyday life has been evolving. METHODS: Hence, in this paper, we present the Inexpensive Node for bed-exit Detection (INBED), a comprehensive, favourable signaling system for bed-exit detection and fall prevention, to support the clinical efforts in terms of fall reduction. The tough requirements for such a system in clinical environments were gathered in close cooperation with geriatricians. RESULTS: The conceptional efforts led to a multi-component system with a core wearable device, attached to the patients, to detect several types of movements such as rising, restlessness and-in the worst case-falling. Occurring events are forwarded to the nursing staff immediately by using a modular, self-organizing and dependable wireless infrastructure. Both, the hardware and software of the entire INBED system as well as the particular design process are discussed in detail. Moreover, a trail test of the system is presented. CONCLUSIONS: The INBED system can help to relieve the nursing staff significantly while the personal freedom of movement and the privacy of patients is increased compared to similar systems.

Should Degree Programs in Biomedical and Health Informatics be Dedicated or Integrated? : Reflections and Recommendations after more than 40 Years of Medical Informatics Education at TU Braunschweig, including 10 Years of B.Sc. and 15 Years of M.Sc. Integrated Degree Curricula.

Education in biomedical and health informatics (BMHI) has been established in many countries throughout the world. For degree programs in BMHI we can distinguish between those that are completely stand-alone or dedicated to the discipline vs. those that are integrated within another program. After running integrated degree medical informatics programs at TU Braunschweig for 10 years at the B.Sc. and for 15 years at the M.Sc level, we (1) report about this educational approach, (2) analyze recommendations on, implementations of, and experiences with degree educational programs in BMHI worldwide, (3) summarize our lessons learned with the integrated approach at TU Braunschweig, and (4) suggest an answer to the question, whether degree programs in biomedical and health informatics should be dedicated or integrated. According to our experience at TU Braunschweig and based on our analysis of publications, there is a clear dominance of dedicated degree programs in BMHI. The specialization in medical informatics within a computer science program, as offered at TU Braunschweig, may be a good way of implementing an integrated, informatics-based approach to medical informatics, in particular if a dual degree option can be chosen. The option of curricula leading to double degrees, i.e. in this case to two separate degrees in computer science and in medical informatics might, however, be a better solution.

Feasibility study of a sensor-based autonomous load control exercise training system for COPD patients.

Decision support systems (DSSs) which are able to automatically supervise and control physical exercise training of patients affected by chronic obstructive pulmonary disease (COPD) are regarded as a novel method to promote rehabilitation. The objective of our research work for this paper was to evaluate the feasibility of a rule-based DSS for autonomous bicycle ergometer training of COPD patients. Load control is based on real-time analysis of sensor parameters oxygen saturation and heart rate. Ten COPD patients have participated in a study, performing altogether 18 training sessions. On average, 7.4 rules were fired in each training session. Four sessions had to be stopped for different reasons. The average ergometer training load ranged between 31 and 47 W. The average percentage of heart rate in or lower than the intended target zone was 45.9 and 41.6%, respectively. The average patient-perceived Borg value was 12.6±2.4. Patients reported a high satisfaction for the automatically controlled training. With the help of the DSS, patients may change their training place from a rehabilitation center to their own homes. More studies are needed to assess long-term clinical and motivational effects of the DSS in home environment.

A collaboration tool based on SNOCAP-HET.

Health enabling technologies and ambient assisted living are important fields in biomedical informatics. In this context, a huge variety of analysis methods are applied. Neither is a suitable structuring of these methods available, nor is an aid known for selecting appropriate methods for a given set of data specifying a context and a problem. The goal of the present paper is to present a prototype of a semantic collaboration tool which is based on the Systematic Nomenclature for Contexts, Analysis Methods and Problems in Health-Enabling Technologies (SNOCAP-HET). This tool can be seen as a first step towards an assistance system for decision support within SNOCAP-HET. We present aspects of the selection and modeling process of our tool and discuss its benefits and appealing tasks for further research. Moreover we present a number of already planned and some unspecified upcoming steps which should optimize SNOCAP-HET in the future.

Assessing the prognoses on Health care in the information society 2013--thirteen years after.

Health care and information technology in health care is advancing at tremendous speed. We analysed whether the prognoses by Haux et al. - first presented in 2000 and published in 2002 - have been fulfilled in 2013 and which might be the reasons for match or mismatch. Twenty international experts in biomedical and health informatics met in May 2013 in a workshop to discuss match or mismatch of each of the 71 prognoses. After this meeting a web-based survey among workshop participants took place. Thirty-three prognoses were assessed matching; they reflect e.g. that there is good progress in storing patient data electronically in health care institutions. Twenty-three prognoses were assessed mismatching; they reflect e.g. that telemedicine and home monitoring as well as electronic exchange of patient data between institutions is not established as widespread as expected. Fifteen prognoses were assessed neither matching nor mismatching. ICT tools have considerably influenced health care in the last decade, but in many cases not as far as it was expected by Haux et al. in 2002. In most cases this is not a matter of the availability of technical solutions but of organizational and ethical issues. We need innovative and modern information system architectures which support multiple use of data for patient care as well as for research and reporting and which are able to integrate data from home monitoring into a patient centered health record. Since innovative technology is available the efficient and wide-spread use in health care has to be enabled by systematic information management.

Past and next 10 years of medical informatics.

More than 10 years ago Haux et al. tried to answer the question how health care provision will look like in the year 2013. A follow-up workshop was held in Braunschweig, Germany, for 2 days in May, 2013, with 20 invited international experts in biomedical and health informatics. Among other things it had the objectives to discuss the suggested goals and measures of 2002 and how priorities on MI research in this context should be set from the viewpoint of today. The goals from 2002 are now as up-to-date as they were then. The experts stated that the three goals: "patient-centred recording and use of medical data for cooperative care"; "process-integrated decision support through current medical knowledge" and "comprehensive use of patient data for research and health care reporting" have not been reached yet and are still relevant. A new goal for ICT in health care should be the support of patient centred personalized (individual) medicine. MI as an academic discipline carries out research concerning tools that support health care professionals in their work. This research should be carried out without the pressure that it should lead to systems that are immediately and directly accepted in practice.

DR.BEAT: Initial Functional Testing of a BCG Wearable Prototype for Recording Ballistocardiographic Signals.

DR.BEAT ("Digital Research on Ballistocardiography for Extraterrestrial And Terrestrial use") develops a miniaturized sensor system with signal processing to interpret ballistocardiographic signals and implements an application oriented user interface. Presented is a breadboard prototype's functional tests with regard to data completeness and plausibility. The analysis confirmed a reliability of 99.99995% over the tests and the signals displayed the expected heart-specific characteristics. These results support the ethical justifiability of an initial study.

DR.BEAT: Rule-Based Algorithm for SCG Analysis Without ECG Reference.

The DR.BEAT project aims to develop an accelerometer-based, wearable sensor system for measuring ballistocardiographic (BCG) signals, coupled with signal processing and visualization, to support cardiac health monitoring. A rule-based heartbeat detection was developed to enable the derivation of health parameters independent of an existing reference. This paper outlines the algorithm's methodology and provides an initial evaluation of its performance based on seismocardiographic (SCG) measurements obtained from an initial study involving twelve heart-healthy adults. On average, 87.6% of the heartbeats over all measurements, 97.6% of the heartbeats at rest and 71.9% of the heartbeats during physical stress could be detected.

An Open Information Model-Based Repository for Sustainable Re-Use of Heterogeneous Pandemics Research Data.

The COVID-19 Research Network Lower Saxony (COFONI) is a German state network of experts in Coronavirus research and development of strategies for future pandemics. One of the pillars of the COFONI technology platform is its established research data repository (Available at https://forschungsdb.cofoni.de/), which enables provision of pseudonymised data and cross-location data retrieval for heterogeneous datasets. The platform consistently uses open standards (openEHR) and open source components (EHRbase) for its data repository, taking into account the FAIR criteria. Available data include both clinical and socio-demographic patient information. A comprehensive AQL query builder interface and an integrated research request process enable new research approaches, rapid cohort assembly and customized data export for researchers from participating institutions. Our flexible and scalable platform approach can be regarded as a blueprint. It contributes, to pandemic preparedness by providing easily accessible cross-location research data in a fully standardised and open representation.

Design and implementation of the standards-based personal intelligent self-management system (PICS).

Against the background of demographic change and a diminishing care workforce there is a growing need for personalized decision support. The aim of this paper is to describe the design and implementation of the standards-based personal intelligent care systems (PICS). PICS makes consistent use of internationally accepted standards such as the Health Level 7 (HL7) Arden syntax for the representation of the decision logic, HL7 Clinical Document Architecture for information representation and is based on a open-source service-oriented architecture framework and a business process management system. Its functionality is exemplified for the application scenario of a patient suffering from congestive heart failure. Several vital signs sensors provide data for the decision support system, and a number of flexible communication channels are available for interaction with patient or caregiver. PICS is a standards-based, open and flexible system enabling personalized decision support. Further development will include the implementation of components on small computers and sensor nodes.

[Health enabling technologies and intelligent environments in rehabilitation]. / Assistierende Gesundheitstechnologien und intelligente Umgebungen in der Rehabilitation.

BACKGROUND: The increasing digitalization of society is having an impact on medicine. People increasingly use digital devices and services for various purposes (e.g., sports, security, convenience). Ubiquity, a strong degree of connectivity and high context sensitivity are creating intelligent environments that generate data about individuals. Suitable evaluation algorithms can extract information about the personal health status that can be used for diagnostics and treatment. Gamification methods allow patients to be more actively involved in their recovery, which can have a positive effect on adherence. Particularly in the field of rehabilitation medicine, which often affects and interacts with the personal living environment, the use of this information can make a difference. OBJECTIVE: Using specific examples of the application of assistive health technologies and intelligent environments in rehabilitation medicine, the current state of development is presented and the possible future research directions and needs for action in this field are presented in a practical way. MATERIAL AND METHODS: Three exemplary research projects introduce the topic, are embedded in the current state of research and allow a projection into the future against the background of many years of experience. RESULTS: The reported projects show not only the technical feasibility but also individually the medical effectiveness of interventions. CONCLUSION: Finally, an analysis of the barriers that have so far prevented a more intensive use of the technologies and how these might be countered is carried out.

DR.BEAT: First Insights into a Study to Collect Baseline BCG Data with a Sensor-Based Wearable Prototype in Heart-Healthy Adults.

The DR.BEAT project aims at the further development of a measurement system for recording ballistocardiographic signals into a body-worn sensor system combined with extensive signal processing, data evaluation and visualization. With a first breadboard prototype, an explorative feasibility study for acquiring initial signals of healthy cardiac activity in adults was performed. This paper briefly presents the DR.BEAT project, the breadboard prototype, the study conducted, and initial insights into the study results. The signals obtained in the study exhibit the seismocardiographic characteristics as reported in the literature and form the basis for further development of the hardware as well as the pre-processing and automated analysis algorithms in the DR.BEAT project.Clinical Relevance- The characteristics of ballisto- and seismocardiographic signals allow to infer about the mechanical work of the heart. The development of a body-worn sensor system to record ballisto- and seismocardiographic signals, compact enough for everyday wear, enables the acquisition of heart-specific parameters in terrestrial as well as extraterrestrial application scenarios. Combined with extensive signal analysis and visualization, it holds the potential to monitor heart health in a variety of contexts and support its maintenance and improvement.

Towards a Recommendation for Good Health Data Modeling (GHDM) - Results of Expert Interviews.

Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling. Expert interviews with five experts were conducted and analyzed using qualitative content analysis. Based on the condensed categories "governance", "modeling" and "standards", the project team generated eight hypotheses for recommendations on health data modeling. In addition, relevant framework conditions such as different roles, international cooperation, education/training and political influence were identified. Although emerging from interviewing a small convenience sample of experts, the results help to plan more extensive data collections and to create recommendations for health data modeling.

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

BACKGROUND: Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). METHODS: A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. RESULTS: The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. CONCLUSIONS: Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.

A literature overview and comparison of driving impairing condition assessment approaches by health enabling technologies.

Due to demographic change, more elderly people have the need to preserve and support mobility by car despite age-related functional limitations. Since accidents by the elderly are primarily caused by age related limitations, and not by careless or irresponsible behavior, it may be beneficial to detect driving impairing conditions. The presented review gives an overview of technologies to detect driving impairing conditions like drowsiness and stress or excessive demand. A comparison of the approaches to detect these conditions suggests that a combination of approaches is the most feasible method. However, there are still few systems that focus on the elderly.

Compiling Criteria for Assessing Essential Aspects of Home Exercise Performance: A Questionnaire-Based Approach.

Health-enabling technologies (HET) have high potential in rehabilitation to support patients performing their home exercises. The modeling of human movements as well as the modelling of quality criteria of an exercise performance remains challenging when implementing HETs. A combination of data-driven approaches and knowledge-based methods may deliver new insights. This requires structured quality assessments of concrete exercise performances from a therapists' point of view. Therefore, a structured, easy to use questionnaire to assess home exercise performances is developed and implemented. The questionnaire consists of eight items in three categories: (1-4) overall assessment of quality and quantity, (5-7) need for correction, and (8) correction hints. The collected data will be the basis for mathematical modeling of home exercise performance assessment as foundation for the development of patient supporting HETs.

Factors for Individualization of Therapeutic Exercises for the Design of Health-Enabling Technologies.

Designing health-enabling technologies (HETs) to support individualized physiotherapeutic exercises requires comprehensive knowledge of bio-psycho-social factors to be considered. Therefore, this review identified factors for individualization of therapeutic exercises in patients with musculoskeletal shoulder disorders in peer-reviewed articles searched in MEDLINE. The final full-text analysis included 16 of 335 search results and extracted nineteen main categories of individualization factors. The most frequently identified main categories include progression of exercises, exercise framework, and assessment. An iterative approach with constant reassessments represents the key principle for the process of individualization. Categories that are difficult to standardize were rarely mentioned, but should also be considered. The identified factors can improve the design-process of HETs by sensitizing developers, enable further formal modelling, and support communication between developers, physiotherapists, and patients.

Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients.

BACKGROUND: Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. METHODS: In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. RESULTS: Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. CONCLUSIONS: Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.

A nomenclature for the analysis of continuous sensor and other data in the context of health-enabling technologies.

Due to the progress in technology, it is possible to capture continuous sensor data pervasively and ubiquitously. In the area of health-enabling and ambient assisted technologies we are faced with the problem of analyzing these data in order to improve or at least maintain the health status of patients. But due to the interdisciplinarity of this field every discipline makes use of their own analyzing methods. In fact, the choice of a certain analyzing method often solely depends on the set of methods known to the data analyst. It would be an advantage if the data analyst would know about all available analyzing methods and their advantages and disadvantages when applied to the manifold of data. In this paper we propose a nomenclature that structures existing analyzing methods and assists in the choice of a certain method that fits to a given measurement context and a given problem.