Blockchain technology is increasingly recognized as a promising solution for managing health-related data, particularly in promoting well-being through physical activity. This is becoming more significant as the Internet of Things (IoT) and sport monitoring sensors continue to expand and become more available, leading to a growing number of users in sports and prolonged usage of these devices, which continuously capture large volumes of physical activity data. The substantial volume of data generated in sports and physical activities, combined with distinct concerns compared to medical and health-related information, makes this domain a unique case for blockchain applications. This paper presents a systematic review of blockchain applications in physical activity, exercise-based rehabilitation, fitness, sport, and exercise-based therapeutics (PARFSET). It specifically focuses on examining their quality attributes, including privacy, security, accountability, personalization, adherence, and extensibility. Our objective is to establish a foundational understanding of the benefits of a blockchain in PARFSET domains, particularly following the decline in initial hype for blockchain technology. We aim to provide a clearer perspective on potential applications, future advancements, and research directions. To this end, we assess the maturity levels of blockchain adoption in these areas and highlight specific examples where a blockchain contributes to enhanced data protection, user-centered customization, trust through accountability, and system scalability. Additionally, we present a hypothetical illustrative case to demonstrate how blockchain applications and their quality outcomes can be effectively integrated. Finally, the paper explores the challenges associated with blockchain implementation and outlines potential directions for future research.

Varroa mites are a major global threat to honeybee colonies. Combining digital twins with scenario-generating models can be an enabler of precision apiculture, allowing for monitoring Varroa spread, generating treatment scenarios under varying conditions, and running remote interventions. This paper presents the conceptual design of this system for large-scale Varroa management in honeybee apiaries, with initial validation conducted through simulations and feasibility analysis. The design followed a design research framework. The proposed system integrates a wireless sensor network for continuous hive sensing, image capture, and remote actuation of treatment. It employs generative time-series models to forecast colony dynamics and a statistical network model to represent inter-colony spread; together, they support spread scenario prediction and what-if evaluations of treatments. The system evolves through continuous updates from field data, improving the accuracy of spread and treatment models over time. As part of our design research, an early feasibility assessment was carried out through the generation of synthetic data for spread model pretraining. In addition, a node-level energy budget for sensing, communication, and in-hive treatment was developed and matched with battery capacity and life calculations. Overall, this work outlines a path toward real-time, data-driven Varroa management across apiary networks, from regional to cross-border scales. 

We must adapt to the new educational landscape shaped by Generative AI, especially Large Language Models like ChatGPT. This shift disrupts traditional learning models and requires a major redesign of learning activities. We also need a new evaluation framework to determine when using Generative AI in education is appropriate or even encouraged, and when it is prohibited. Instead of resisting the inevitable, we should clearly communicate what is acceptable and what is not.

Background: The use of health technology by older people is coming increasingly in focus with the demographic changes. Health information technology is generally perceived as an important factor in enabling increased quality of life and reducing the cost of care for this group. Age-appropriate design and facilitation of technology adoption are important to ensure functionality and removal of various barriers to usage. Development of assessment tools and instruments for evaluating older persons' technology adoption and usage as well as measuring the effects of the interventions are of high priority. Both usability and acceptance of a specific technology or service are important factors in evaluating the impact of a health information technology intervention. Psychometric measures are seldom included in evaluations of health technology. However, basic attitudes and sentiments toward technology (eg, technophilia) could be argued to influence both the level of satisfaction with the technology itself as well as the perception of the health intervention outcome. Objective: The purpose of this study is to develop a reduced and refined instrument for measuring older people's attitudes and enthusiasm for technology based on relevant existing instruments for measuring technophilia A requirement of the new instrument is that it should be short and simple to make it usable for evaluation of health technology for older people. Methods: Initial items for the TechPH questionnaire were drawn from a content analysis of relevant existing technophilia measure instruments. An exploratory factor analysis was conducted in a random selection of persons aged 65 years or older (N=374) on eight initial items. The scale was reduced to six items, and the internal consistency and reliability of the scale were examined. Further validation was made by a confirmatory factor analysis (CFA). Results: The exploratory factor analysis resulted in two factors. These factors were analyzed and labeled techEnthusiasm and techAnxiety. They demonstrated relatively good internal consistency (Cronbach alpha=.72 and .68, respectively). The factors were confirmed in the CFA and showed good model fit (chi(2)(8)=21.2, chi(2)/df=2.65, comparative fit index=0.97, adjusted goodness-of-fit index=0.95, root mean square error of approximation=0.067, standardized root mean square residual=0.036). Conclusions: The construed TechPH score showed expected relations to external real-world criteria, and the two factors showed interesting internal relations. Different technophilia personality traits distinguish clusters with different behaviors of adaptation as well as usage of new technology. Whether there is an independent association with the TechPH score against outcomes in health technology projects needs to be shown in further studies. The instrument must also be validated in different contexts, such as other countries.

Health information systems are our technological response to the growing demand for health care. However, their success in their mission can be challenging due to the complexity of evaluating technological interventions in health care. In the series of studies compiled in this dissertation, we looked at the evaluation of these systems. We focused on the evaluation of factors that lead to success, where success is indicated by user satisfaction and can be induced by both intervention-specific and individual-specific factors.

Study 1 developed a method, called UVON, to elicit and organise the user-demanded qualities in the outcomes of the health information system intervention. Through the application of the UVON method in the FI-STAR project, an EU project which developed and deployed seven e-health applications in seven member countries, ten categories of quality and their subcategories were identified. These qualities formed two questionnaires, specific to the patient and health professional users. Through the questionnaires, the patients and health-professionals users evaluated and graded both the occurrence of those demanded qualities in the project outcomes and their general satisfaction.

Study 2 analysed the survey results to find out which of those ten qualities have the highest impact on satisfaction or can predict it better. Two partial least squares structural equation modelling (PLS-SEM) models were constructed, for the patient and health professionals, based on the Unified eValuation using ONtology (UVON) and survey outputs. The models showed that effectiveness is an important quality in creating satisfaction for both user groups. Besides, affordability for the health professionals and efficiency plus safety for the patients were the most influential. A satisfaction index is also introduced for simple and fast inferring of the changes in the outcome qualities.

Study 5 recruited outputs and learnings from studies 1 and 2 to design a system that partially automates the process of evaluating success factors in health information systems, making it continuous and real-time, and replacing hard-to-run surveys with automatically captured indicators and analytics.

Study 3 focused on individual-specific factors in using health information systems, particularly the technophilia personality trait. A short six-items instrument, called TechPH, was designed to measure technophilia in users, tuned for older users. The study recruited empirical data from the Swedish National Study on Aging and Care (SNAC) project. Two factors, labelled techAnxiety and techEnthusiams, are identified by the factor analysis method. A TechPH score was introduced as a scalar measurement of technophilia.

Study 4 elicited and discussed the ethical challenges of evaluating and researching health information systems. Both a scoping review and a novel systematic postulation approach were recruited to identify twenty ethical challenges. The identified ethical challenges were discussed and mapped into a three-dimensional space of evaluation stages, demanded qualities, and major involving entities (stakeholder and artefacts), which fosters further postulation of ethical challenges.

Background:

Several models suggest how the qualities of a product or service influence user satisfaction. Models, such as the Customer Satisfaction Index (CSI), Technology Acceptance Model (TAM), and Delone and McLean Information Systems Success (D&M IS), demonstrate those relations and have been used in the context of health information systems.

Objective:

We want to investigate which qualities foster greater satisfaction among patient and professional users. In addition, we are interested in knowing to what extent improvement in those qualities can explain user satisfaction and if this makes user satisfaction a proxy indicator of those qualities.

Methods:

The Unified eValuation using ONtology (UVON) method was utilised to construct an ontology of the required qualities for seven e-health applications being developed in the FI-STAR project, a European Union (EU) project in e-health. The e-health applications were deployed across seven EU countries. The ontology included and unified the required qualities of those systems together with the aspects suggested by the Model for ASsessment of Telemedicine applications (MAST) evaluation framework. Two similar questionnaires, for 87 patient users and 31 health professional users, were elicited from the ontology. In the questionnaires, user was asked if the system has improved the specified qualities and if the user was satisfied with the system. The results were analysed using Kendall correlation coefficients matrices, incorporating the quality and satisfaction aspects. For the next step, two Partial Least Squares Structural Equation Modelling (PLS-SEM) path models were developed using the quality and satisfaction measure variables and the latent construct variables that were suggested by the UVON method.

Results:

Most of the quality aspects grouped by the UVON method are highly correlated. Strong correlations in each group suggest that the grouped qualities can be measures which reflect a latent quality construct. The PLS-SEM path analysis for the patients reveals that the effectiveness, safety, and efficiency of treatment provided by the system are the most influential qualities in achieving and predicting user satisfaction. For the professional users, effectiveness and affordability are the most influential. The parameters of the PLS-SEM that are calculated allow for the measurement of a user satisfaction index similar to CSI for similar health information systems.

Conclusions:

For both patients and professionals, the effectiveness of systems highly contributes to their satisfaction. Patients care about improvements in safety and efficiency, while professionals care about improvements in the affordability of treatments with health information systems. User satisfaction is reflected more in the users' evaluation of system output and fulfilment of expectations, but slightly less in how far the system is from ideal. Investigating satisfaction scores can be a simple, fast way to infer if the system has improved the abovementioned qualities in treatment and care.

Background Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. Objective The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. Method To achieve our goal we carried out a systematic literature review, in which three large databases -Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. Results In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer's disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Conclusions Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies' different contexts. © 2017 Dallora et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Background: There are several frameworks that attempt to address the challenges of evaluation of health information systems by offering models, methods, and guidelines about what to evaluate, how to evaluate, and how to report the evaluation results. Model-based evaluation frameworks usually suggest universally applicable evaluation aspects but do not consider case-specific aspects. On the other hand, evaluation frameworks that are case specific, by eliciting user requirements, limit their output to the evaluation aspects suggested by the users in the early phases of system development. In addition, these case-specific approaches extract different sets of evaluation aspects from each case, making it challenging to collectively compare, unify, or aggregate the evaluation of a set of heterogeneous health information systems.

Objectives: The aim of this paper is to find a method capable of suggesting evaluation aspects for a set of one or more health information systems—whether similar or heterogeneous—by organizing, unifying, and aggregating the quality attributes extracted from those systems and from an external evaluation framework.

Methods: On the basis of the available literature in semantic networks and ontologies, a method (called Unified eValuation using Ontology; UVON) was developed that can organize, unify, and aggregate the quality attributes of several health information systems into a tree-style ontology structure. The method was extended to integrate its generated ontology with the evaluation aspects suggested by model-based evaluation frameworks. An approach was developed to extract evaluation aspects from the ontology that also considers evaluation case practicalities such as the maximum number of evaluation aspects to be measured or their required degree of specificity. The method was applied and tested in Future Internet Social and Technological Alignment Research (FI-STAR), a project of 7 cloud-based eHealth applications that were developed and deployed across European Union countries.

Results: The relevance of the evaluation aspects created by the UVON method for the FI-STAR project was validated by the corresponding stakeholders of each case. These evaluation aspects were extracted from a UVON-generated ontology structure that reflects both the internally declared required quality attributes in the 7 eHealth applications of the FI-STAR project and the evaluation aspects recommended by the Model for ASsessment of Telemedicine applications (MAST) evaluation framework. The extracted evaluation aspects were used to create questionnaires (for the corresponding patients and health professionals) to evaluate each individual case and the whole of the FI-STAR project.

Conclusions: The UVON method can provide a relevant set of evaluation aspects for a heterogeneous set of health information systems by organizing, unifying, and aggregating the quality attributes through ontological structures. Those quality attributes can be either suggested by evaluation models or elicited from the stakeholders of those systems in the form of system requirements. The method continues to be systematic, context sensitive, and relevant across a heterogeneous set of health information systems.

OBJECTIVE: The objective of this paper is to investigate the goals and variables employed in the machine learning and microsimulation studies for the prognosis of dementia. METHOD: According to preset protocols, the Pubmed, Socups and Web of Science databases were searched to find studies that matched the defined inclusion/exclusion criteria, and then its references were checked for new studies. A quality checklist assessed the selected studies, and removed the low quality ones. The remaining ones (included set) had their data extracted and summarized. RESULTS: The summary of the data of the 37 included studies showed that the most common goal of the selected studies was the prediction of the conversion from mild cognitive impairment to Alzheimer's Disease, for studies that used machine learning, and cost estimation for the microsimulation ones. About the variables, neuroimaging was the most frequent used. CONCLUSIONS: The systematic literature review showed clear trends in prognosis of dementia research in what concerns machine learning techniques and microsimulation.

Designing systems working in health care needs complying with the heterogeneous, overlapping, non-overlapping, competing, or even contradicting requirements expressed by the various actors of the health care complex environment, including regulatory bodies. The unification method introduced in this paper, utilized ontological struc- tures to unify heterogeneous requirements in different levels of ab- straction. Also the weighting and threshold algorithms defined upon the ontology structure allows to both prioritize the requirements and align design resources upon that priority, at the same time to enforce regulatory requirements in an easy, clear and integrated way and reject designs which cannot comply with them. Application of the method introduced in this paper is not limited to health care, but it might be applied in design for any heterogeneous environment.