Impact of information technology supported serious leisure gardening on the wellbeing of older adults: The Turntable project.

OBJECTIVE: The study presented in this paper aimed to assess the effect of an Information Technology enabled community gardening program for older adults, developed by an international consortium. METHODS: We have executed a quantitative, pre- and post-test field trial with older adult volunteers to test the proposed programme in two European countries, Italy and Belgium (n=98). We used standardized and ad hoc questionnaires to measure changes in the volunteers' mental and psychological state during the trial. The statistical data analysis sought for differences in the pre- and post-test values of the key scores related to the perceived quality of life and benefits of gardening via paired-samples t-tests, and also tried to identify the important factors of significant changes via logistic regression. RESULTS: We found significant improvements in the perceived benefits of gardening and also in the scores computed from the WHO Quality of Life instruments, especially in the social sub-domains. The improvements were associated with the country, age, marital state and education of the volunteers. Higher age or being widow, divorced or single increased the odds of a significant improvement in the scores in more than one sub-domains. CONCLUSION: Though the two trial settings were different in some aspects, the observed significant improvements generally confirmed the positive effects of gardening concerning the perceived quality of life and benefits of gardening.

Multi-level process mining methodology for exploring disease-specific care processes.

BACKGROUND: Public healthcare is a complex domain with many actors and highly variable protocols, which makes traditional process mining tools less effective and calls for specialized methods. AIM: The objective of the work was to develop a generally applicable process mining methodology to explore care processes related to diseases. METHODS: The proposed methodology called Process Mining Methodology for Exploring Disease-specific Care Processes (MEDCP) is based on a systematic, step-wise refinement of the raw event logs by using such a multi-level expert taxonomy of events that encapsulates the professional concepts of the analysis. A treatment process is defined according to domain-specific rules to identify the starting (index) and closing events. Concepts from various levels of the taxonomy support the final process definition for an analysis that can deliver meaningful conclusions for domain experts. RESULTS: The applicability of the methodology was demonstrated on two case studies in the cardiological and oncological care domains, in the public health care system in Hungary over a period of ten years. Thanks to the multi-level taxonomy, these studies successfully identified the most important high-level event sequence patterns and some key anomalies in the national care system, such as the significantly different behavior of low-volume vs. high volume care providers in the oncology study or the geographically connected, homogeneous clusters of providers with similar care spectra in the cardiology study. DISCUSSION: As the case studies showed, the proposed methodology can improve the efficiency of standard process mining methods, and deliver high level conclusions that are easy to interpret by domain experts. System-level insight into health care processes can serve as a basis for the optimisation and long-term planning of the whole care system.

Efficient methods for acute stress detection using heart rate variability data from Ambient Assisted Living sensors.

BACKGROUND: Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices. METHODS: Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed. RESULTS: The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62-94.55% accuracy and 91.77-94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively. CONCLUSION: The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.

Improved Methods for Mid-Term Blood Glucose Level Prediction Using Dietary and Insulin Logs.

Background and Objectives: The daily lifestyle management of diabetes requires accurate predictions of the blood glucose level between meals. The objective of this study was to improve the accuracy achieved by previous work, especially on the mid-term, i.e., 120 to 180 min prediction horizons, for insulin-dependent patients. Materials and Methods: An absorption model-based method is proposed to train an artificial neural network with the bolus and basal insulin dosing and timing, the baseline blood glucose level, the maximal glucose infusion rate, and the total carbohydrate content as parameters. The approach was implemented in various algorithmic setups, and it was validated on data from a small-scale clinical trial with continuous glucose monitoring. Results: Root mean square error results for the mid-term horizons are 1.72 mmol/L (120 min) and 1.95 mmol/L (180 min). The accuracy of the proposed model measured on the clinical data is better than the accuracy reported by any other currently available and comparable models. Conclusions: A relatively short (ca. two weeks) training sample of a continuous glucose monitor and dietary/insulin log is sufficient to provide accurate predictions. For the outpatient application in practice, a hybrid model is proposed that combines the present mid-term method with the authors' previous work for short-term predictions.

[Support of diabetes dietary management and self-management using mobile applications]. / Diabetesesek dietoterápiájának és önmenedzselésének támogatása mobilapplikációk használatával.

The key components of successful diabetes therapy are pharmacotherapy, hospital care and lifestyle education. Lifestyle education, self-management, and composing the right diet can be effectively supported with mobile applications. In this paper Hungarian mobile applications are reviewed and compared to some international competitors. Besides plenty of useful functions some deficiencies are identified, based on dietary recommendations. The related improvements together with clinical trials validating effectiveness and reliability can strengthen medical evidence as well as the penetration of such mobile applications. Orv. Hetil., 2016, 157(29), 1147-1153.

The low FODMAP diet supported by a mobile application for functional gastrointestinal and inflammatory bowel diseases / A mobilapplikációval támogatott alacsony-FODMAP-étrend a funkcionális gastrointestinalis és a gyulladásos bélbetegségek kezelésében

Diseases affecting the gastrointestinal tract such as functional gastrointestinal diseases ­ including irritable bowel syndrome ­ as well as inflammatory bowel diseases are on the rise in Hungary. More and more nutritional recommendations are emerging as part of the therapy, but so far there is no uniform recommendation for the dietary management of these gastrointestinal diseases. Among the dietary recommendations that have been made so far, the low FODMAP diet is noteworthy. FODMAP itself stands for the abbreviation of the initials of fermentable, short-chain, poorly absorbable carbohydrates: fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAP). The low FODMAP diet is a scientifically and clinically proven therapeutic recommendation, which is supported in Hungary by the Ministry of Human Resources Guideline (2020). Research has shown that the low FODMAP diet has been shown to reduce gastrointestinal symptoms. The diet consists of 3 phases, the first of which is a step-by-step list of trigger and non-trigger foods that the doctor, with the help of a dietitian, determines. As this stage of the diet can be the most challenging, it is worth emphasizing that it should be developed in collaboration with a dietitian. The aim of the diet is to find a balance between keeping symptoms at a low level and expanding the diet. Low FODMAP diet has been shown to be an effective, successful, and accepted nutritional intervention in the management of symptoms of functional and inflammatory bowel disease. Its use can improve the success of pharmacological interventions and increase patient compliance, hence the need to expand the widespread dissemination of the diet. A mobile app developed by a research team at Monash University will support self-management and practical implementation of the diet and increase adherence to nutritional therapy.

Applications of medical intelligence in remote monitoring.

Prevention and rehabilitation efficiency can greatly benefit from the application of intelligent, 24 hour tele-diagnostics and tele-care information systems. Tele-monitoring also supports a new level of medical supervision over the patient's lifestyle. In this paper we briefly present the architecture and development phase results of the Alpha remote monitoring system. The novelty of the system is the unified and flexible processing of various signals retrieved from modern, unobtrusive devices in an efficient signal abstraction framework. The signals include PIR motion sensors that record patient movement in the home, physiological signals and also patient responses in various tests performed on the GUI of the central home unit. We have developed and tested the prototype system with promising results.

An ontological modeling approach to cerebrovascular disease studies: the NEUROWEB case.

The NEUROWEB project supports cerebrovascular researchers' association studies, intended as the search for statistical correlations between a feature (e.g., a genotype) and a phenotype. In this project the phenotype refers to the patients' pathological state, and thus it is formulated on the basis of the clinical data collected during the diagnostic activity. In order to enhance the statistical robustness of the association inquiries, the project involves four European Union clinical institutions. Each institution provides its proprietary repository, storing patients' data. Although all sites comply with common diagnostic guidelines, they also adopt specific protocols, resulting in partially discrepant repository contents. Therefore, in order to effectively exploit NEUROWEB data for association studies, it is necessary to provide a framework for the phenotype formulation, grounded on the clinical repository content which explicitly addresses the inherent integration problem. To that end, we developed an ontological model for cerebrovascular phenotypes, the NEUROWEB Reference Ontology, composed of three layers. The top-layer (Top Phenotypes) is an expert-based cerebrovascular disease taxonomy. The middle-layer deconstructs the Top Phenotypes into more elementary phenotypes (Low Phenotypes) and general-use medical concepts such as anatomical parts and topological concepts. The bottom-layer (Core Data Set, or CDS) comprises the clinical indicators required for cerebrovascular disorder diagnosis. Low Phenotypes are connected to the bottom-layer (CDS) by specifying what combination of CDS values is required for their existence. Finally, CDS elements are mapped to the local repositories of clinical data. The NEUROWEB system exploits the Reference Ontology to query the different repositories and to retrieve patients characterized by a common phenotype.

After-meal blood glucose level prediction using an absorption model for neural network training.

BACKGROUND: Diabetes Mellitus outpatients would benefit from a lifestyle support tool that delivers reliable short term Blood Glucose Level (BGL) predictions. AIM: To develop a method for BGL prediction based on the baseline BGL, the insulin dosing and a dietary log. METHODS: A new training method is proposed for a neural network in which an absorption model is applied that uses the nutrient contents of meals. The numerical characteristics of the computed absorption curve are fed to the neural network as training inputs along with the applied insulin doses and BGL evolution measured by a Continuous Glucose Monitoring System. For comparison, another version of the training in which raw carbohydrate values are used as dietary inputs has also been implemented. The method was validated in a clinical trial with 5 patients using a total of 167 meals. RESULTS: It was found that the proposed method performed significantly better on the 60- and 120-min prediction horizons, with a Root Mean Square Error of 1.12 mmol/l and 1.75 mmol/l, respectively, and more than 96% of the predicted values falling in the 'clinically acceptable' class according to clinical practice. These results surpass those published results to which our method is directly comparable, and also those of the carbohydrate-only version (1.81 mmol/l and 2.53 mmol/l). CONCLUSION: The integration of the absorption model in the training process has successfully contributed to the success of the model. Future research will focus on a new trial with more patients to verify these promising results.

Blood Glucose Level Prediction for Diabetics Based on Nutrition and Insulin Administration Logs Using Personalized Mathematical Models.

According to recent surveys, the current ways of diabetics trying to estimate their insulin need based on experience and conjecture are sometimes inefficient in practice. This paper proposes a prediction algorithm and presents the validation of the model in outpatient care. The algorithm consists of two state-of-the-art models that calculate nutrition absorption and glycaemia including insulin evolution. The combined model is extended with personalized parameter training including genetic algorithm and Nelder-Mead method, and a more realistic, diurnal parameter profile as a representation of the natural biorhythm. This method implemented in a user-friendly application can help diabetics calculate their insulin need. The tests were performed on a data set including a clinical trial involving more than 20 diabetic patients. We experienced 55% improvement in the results due to model training compared to the tests based on literature parameters. In the best case, 92.5% of the predicted blood glucose level values were in the range of clinically acceptable errors, which means around 2.8 mmol/l root mean square error. The results of the validation based on outpatient data are promising compared to others found in the literature. Handling other important factors such as physical activity and stress remains a challenge for future research.

Correlation Clustering of Stable Angina Clinical Care Patterns for 506 Thousand Patients.

Objectives: Our goal was to apply statistical and network science techniques to depict how the clinical pathways of patients can be used to characterize the practices of care providers. Methods: We included the data of 506,087 patients who underwent procedures related to ischemic heart disease. Patients were assigned to one of the 136 primary health-care centers using a voting scheme based on their residence. The clinical pathways were classified, and the spectrum of the pathway types was computed for each center, then a network was built with the centers as nodes and spectrum correlations as edge weights. Then Louvain clustering was used to group centers with similar pathway spectra. Results: We identified 3 clusters with rather distinct characteristics that occupy quite compact spatial areas, though no geographical information was used in clustering. Network analysis and hierarchical clustering show the dominance of medical university clinics in each cluster. Conclusion: Though clinical guidelines provide a uniform regulation for medical decisions, doctors have great freedom in daily clinical practice. This freedom leads to regional preferences of certain clinical pathways, the intercenter professional links, and geographical locality and coupled with quantifiable consequences in terms of care costs and periprocedural risk of patients.

Stress Detection Using Low Cost Heart Rate Sensors.

The automated detection of stress is a central problem for ambient assisted living solutions. The paper presents the concepts and results of two studies targeted at stress detection with a low cost heart rate sensor, a chest belt. In the device validation study ( n = 5), we compared heart rate data and other features from the belt to those measured by a gold standard device to assess the reliability of the sensor. With simple synchronization and data cleaning algorithm, we were able to select highly (>97%) correlated, low average error (2.2%) data segments of considerable length from the chest data for further processing. The protocol for the clinical study ( n = 46) included a relax phase followed by a phase with provoked mental stress, 10 minutes each. We developed a simple method for the detection of the stress using only three time-domain features of the heart rate signal. The method produced accuracy of 74.6%, sensitivity of 75.0%, and specificity of 74.2%, which is impressive compared to the performance of two state-of-the-art methods run on the same data. Since the proposed method uses only time-domain features, it can be efficiently implemented on mobile devices.

Methodological studies related to cardiovascular risk assessment.

Based on Framingham risk estimating equations by computer simulation graphical tools were gained allowing reasonable decision threshold adjustments in cardiovascular screening procedures. Results quantitatively characterize the percentage of the population selected for detailed evaluation vs. the decision threshold in a hypothetical screening procedure. In the second part of the study, the effect of blood pressure (BP) measurement uncertainties on coronary heart disease incidence estimates was characterized. According to our results the use of daily BP averages instead of instantaneous measurements has a significant impact on the risk estimation reliability.

Medical information systems for cardiac risk assessment and remote monitoring.

Remote monitoring and interactive remote counselling provide a cost-effective and comfortable means of medical care. The paper presents the first experiences of two such systems, both in the field of cardiovascular diseases. The prototype medical instruments, the database design and the user interfaces are elaborated. The project runs until mid-2004.

The effect of the waiting times on the patient pathways for patients with suspected coronary artery disease.

The paper describes the investigation of the Hungarian public administrative health databases with the aim to identify hidden correspondences in the patients' evaluation pathways for patients with suspected coronary artery disease (CAD). In our current work we investigated the effect of the waiting times of invasive and non-invasive investigations in the evaluation pathways of patients with suspected CAD. We found a considerable correlation between waiting times and the further course of the patients.

The impact of geographical distances to coronary angiography laboratories on the patient evaluation pathways in patients with suspected coronary artery disease. Results from a population-based study in Hungary.

INTRODUCTION: Coronary artery disease (CAD) has been a leading cause of death in the western world for the last few decades, despite significant improvements in treatment and management. Diagnostic algorithms for the evaluation of patients with suspected CAD are based on available guidelines. AIM: To evaluate the impact of geographical distances to coronary angiography laboratories on the patient evaluation pathways in patients with suspected CAD, from a population-based study in Hungary. MATERIAL AND METHODS: Depersonalised data of 29,202 patients identified by their pseudo-social security number were analysed. All patients underwent coronary angiography as an initial direct invasive investigation (DI) following an at least half-year-long stable period between 1 January 2004 and 31 December 2008. RESULTS: One hundred and thirty-five dominant primary cardiology centres (PCC) have been identified, from which 85 proved to have sample size more than 100 DIs in tertiary cardiology centres (TCC). The frequency of DIs showed a close correlation with PCC-TCC distances (r = -0.44, p < 0.001). A negative correlation could be demonstrated between the age of patients and PCC-TCC distances (r = -0.45, p < 0.001). Without significant change in the absolute mortality, the relative mortality increased with the increase in PCC-TCC distance (r = 0.25, p < 0.05). CONCLUSIONS: The PCC-TCC distance has an important effect on patient pathways in subjects with suspected CAD.

Combined model for diabetes lifestyle support.

Treatment of diabetes mellitus is a public health related problem of modern healthcare. Surveys show that current methods to estimate the required amount of insulin are quite inefficient in practice as they are based on experience. This paper offers a new approach to predict the glucose level of people with diabetes. It combines two efficient models of the literature: one for nutrient absorption and one for glucose control. The combination of them tracks the blood sugar level considering nutrition composition, applied insulin and initial glucose level. Compared to already existing mixed meal models, the current version takes into account a more detailed nutrition composition (protein, lipid, monosaccharide, fiber and starch) supported by our expert dietary systems. Although the model gives satisfactory results even with parameter sets taken from literature, parameter training by genetic algorithms yields a better tracking of the patients.

Preclinical tests of an android based dietary logging application.

UNLABELLED: The paper describes the first, preclinical evaluation of a dietary logging application developed at the University of Pannonia, Hungary. The mobile user interface is briefly introduced. The three evaluation phases examined the completeness and contents of the dietary database and the time expenditure of the mobile based diet logging procedure. The results show that although there are substantial individual differences between various dietary databases, the expectable difference with respect to nutrient contents is below 10% on typical institutional menu list. Another important finding is that the time needed to record the meals can be reduced to about 3 minutes daily especially if the user uses set-based search. CONCLUSION: a well designed user interface on a mobile device is a viable and reliable way for a personalized lifestyle support service.