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Introduction Combating physical inactivity and reducing sitting time are one of the principal challenges proposed by public health systems. Gamification has been seen as an innovative, functional and motivating strategy to encourage patients to increase their physical activity (PA) and reduce sedentary lifestyles through behaviour change techniques (BCT). However, the effectiveness of these interventions is not usually studied before their use. The main objective of this study will be to analyse the effectiveness of a gamified mobile application (iGAME) developed in the context of promoting PA and reducing sitting time with the BCT approach, as an intervention of secondary prevention in sedentary patients.Methods and analysis A randomised clinical trial will be conducted among sedentary patients with one of these conditions: non-specific low back pain, cancer survivors and mild depression. The experimental group will receive a 12-week intervention based on a gamified mobile health application using BCT to promote PA and reduce sedentarism. Participants in the control group will be educated about the benefits of PA. The International Physical Activity Questionnaire will be considered the primary outcome. International Sedentary Assessment Tool, EuroQoL-5D, MEDRISK Instruments and consumption of Health System resources will be evaluated as secondary outcomes. Specific questionnaires will be administered depending on the clinical population. Outcomes will be assessed at baseline, at 6 weeks, at the end of the intervention (12 weeks), at 26 weeks and at 52 weeks.Ethics and dissemination The study has been approved by the Portal de Ética de la Investigación Biomédica de Andalucía Ethics Committee (RCT-iGAME 24092020). All participants will be informed about the purpose and content of the study and written informed consent will be completed. The results of this study will be published in a peer-reviewed journal and disseminated electronically and in print.Trial registration number NCT04019119

In the realm of multilingual, AI-powered, real-time optical character recognition systems, this research explores the creation of an optimal, vocabulary-based training dataset. This comprehensive endeavor seeks to encompass a range of criteria: comprehensive language representation, high-quality and diverse data, balanced datasets, contextual understanding, domain-specific adaptation, robustness and noise tolerance, and scalability and extensibility. The approach aims to leverage techniques like convolutional neural networks, recurrent neural networks, convolutional recurrent neural networks, and single visual models for scene text recognition. While focusing on English, Hungarian, and Japanese as representative languages, the proposed methodology can be extended to any existing or even synthesized languages. The development of accurate, efficient, and versatile OCR systems is at the core of this research, offering societal benefits by bridging global communication gaps, ensuring reliability in diverse environments, and demonstrating the adaptability of AI to evolving needs. This work not only mirrors the state of the art in the field but also paves new paths for future innovation, accentuating the importance of sustained research in advancing AI’s potential to shape societal development.

Background: Optimal sports performance requires a balance between intensive training and adequate rest. IMUs provide objective, quantifiable data to analyze performance dynamics, despite the challenges in quantifying athlete training loads. The ability of AI to analyze complex datasets brings innovation to the monitoring and optimization of athlete training cycles. Traditional techniques rely on subjective assessments to prevent overtraining, which can lead to injury and underperformance. IMUs provide objective, quantitative data on athletes’ physical status during action. AI and machine learning can turn these data into useful insights, enabling data-driven athlete performance management. With IMU-generated multivariate time series data, this paper uses AI to construct a robust model for predicting fatigue and stamina. Materials and Methods: IMUs linked to 19 athletes recorded triaxial acceleration, angular velocity, and magnetic orientation throughout repeated sessions. Standardized training included steady-pace runs and fatigue-inducing techniques. The raw time series data were used to train a supervised ML model based on frequency and time-domain characteristics. The performances of Random Forest, Gradient Boosting Machines, and LSTM networks were compared. A feedback loop adjusted the model in real time based on prediction error and bias estimation. Results: The AI model demonstrated high predictive accuracy for fatigue, showing significant correlations between predicted fatigue levels and observed declines in performance. Stamina predictions enabled individualized training adjustments that were in sync with athletes’ physiological thresholds. Bias correction mechanisms proved effective in minimizing systematic prediction errors. Moreover, real-time adaptations of the model led to enhanced training periodization strategies, reducing the risk of overtraining and improving overall athletic performance. Conclusions: In sports performance analytics, the AI-assisted model using IMU multivariate time series data is effective. Training can be tailored and constantly altered because the model accurately predicts fatigue and stamina. AI models can effectively forecast the beginning of weariness before any physical symptoms appear. This allows for timely interventions to prevent overtraining and potential accidents. The model shows an exceptional ability to customize training programs according to the physiological reactions of each athlete and enhance the overall training effectiveness. In addition, the study demonstrated the model’s efficacy in real-time monitoring performance, improving the decision-making abilities of both coaches and athletes. The approach enables ongoing and thorough data analysis, supporting strategic planning for training and competition, resulting in optimized performance outcomes. These findings highlight the revolutionary capability of AI in sports science, offering a future where data-driven methods greatly enhance athlete training and performance management.

Background: Remote diagnosis using collaborative tools have led to multilingual joint working sessions in various domains, including comprehensive health care, and resulting in more inclusive health care services. One of the main challenges is providing a real-time solution for shared documents and presentations on display to improve the efficacy of noninvasive, safe, and far-reaching collaborative models. Classic optical character recognition (OCR) solutions fail when there is a mixture of languages or dialects or in case of the participation of different technical levels and skills. Due to the risk of misunderstandings caused by mistranslations or lack of domain knowledge of the interpreters involved, the technological pipeline also needs artificial intelligence (AI)-supported improvements on the OCR side. This study examines the feasibility of machine learning-supported OCR in a multilingual environment. The novelty of our method is that it provides a solution not only for different speaking languages but also for a mixture of technological languages, using artificially created vocabulary and a custom training data generation approach. Methods: A novel hybrid language vocabulary creation method is utilized in the OCR training process in combination with convolutional recurrent neural networks (CRNNs) and a single visual model for scene text recognition within the patch-wise image tokenization framework (SVTR). Data: In the research, we used a dedicated Python-based data generator built on dedicated collaborative tool-based templates to cover and simulated the real-life variances of remote diagnosis and co-working collaborative sessions with high accuracy. The generated training datasets ranged from 66 k to 8.5 M in size. Twenty-one research results were analyzed. Instruments: Training was conducted by using tuned PaddleOCR with CRNN and SVTR modeling and a domain-specific, customized vocabulary. The Weight & Biases (WANDB) machine learning (ML) platform is used for experiment tracking, dataset versioning, and model evaluation. Based on the evaluations, the training dataset was adjusted by using a different language corpus or/and modifications applied to templates. Results: The machine learning models recognized the multilanguage/hybrid texts with high accuracy. The highest precision scores achieved are 90.25%, 91.35%, and 93.89%. Conclusions: machine learning models for special multilanguages, including languages with artificially made vocabulary, perform consistently with high accuracy.

Many studies have been published in recent years regarding the fact that moderate wine consumption, as a part of a balanced diet can have a beneficial effect on human health. The biologically active components of wine continue to be the subject of intense research today. In this study, the bioactive molecules of Hungarian aszú from the Tokaj wine region were analyzed using high-performance liquid chromatography (HPLC) and investigated in an in vitro model system of endothelial cells induced by bacterial-derived lipopolysaccharide. The HPLC measurements were performed on a reversed phased column with gradient elution. The non-cytotoxic concentration of the active substance was determined based on 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyltetrazolium bromide (MTT)-, apoptosis, and necrosis assays. The antioxidant effect of the extract was determined by evaluating its ability to eliminate ROS. The expressions of the interleukin-(IL)1α, IL1-β, IL-6, and IL-8 pro-inflammatory cytokines and nitric oxide synthase (eNOS) at the mRNA level were evaluated using a quantitative polymerase chain reaction (qPCR). We found that the lipopolysaccharides (LPS)-induced increases in the expressions of the investigated cytokines were significantly suppressed by Hungarian aszú extract, excluding IL-6. In our experimental setup, our treatment had a positive effect on the eNOS expression, which was impaired as a result of the inflammatory manipulation. In our experimental model, the Hungarian aszú extract decreased the LPS-induced increases in the expression of the investigated cytokines and eNOS at the mRNA level, which presumably had a positive effect on the endothelial dysfunction caused by inflammation due to its strong antioxidant and anti-inflammatory effects. Collectively, this research contributes to a more thorough understanding of the bioactive molecules of aszú from the Tokaj wine region.

Background: One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers. Methods: This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances. Results: The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97–99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%. Conclusions: The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently.

BackgroundThere are several mobile health (mHealth) apps in mobile app stores. These apps enter the business-to-customer market with limited controls. Both, apps that users use autonomously and those designed to be recommended by practitioners require an end-user validation to minimize the risk of using apps that are ineffective or harmful. Prior studies have reviewed the most relevant aspects in a tool designed for assessing mHealth app quality, and different options have been developed for this purpose. However, the psychometric properties of the mHealth quality measurement tools, that is, the validity and reliability of the tools for their purpose, also need to be studied. The Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) initiative has developed tools for selecting the most suitable measurement instrument for health outcomes, and one of the main fields of study was their psychometric properties. ObjectiveThis study aims to address and psychometrically analyze, following the COSMIN guideline, the quality of the tools that are used to measure the quality of mHealth apps. MethodsFrom February 1, 2019, to December 31, 2019, 2 reviewers searched PubMed and Embase databases, identifying mHealth app quality measurement tools and all the validation studies associated with each of them. For inclusion, the studies had to be meant to validate a tool designed to assess mHealth apps. Studies that used these tools for the assessment of mHealth apps but did not include any psychometric validation were excluded. The measurement tools were analyzed according to the 10 psychometric properties described in the COSMIN guideline. The dimensions and items analyzed in each tool were also analyzed. ResultsThe initial search showed 3372 articles. Only 10 finally met the inclusion criteria and were chosen for analysis in this review, analyzing 8 measurement tools. Of these tools, 4 validated ≥5 psychometric properties defined in the COSMIN guideline. Although some of the tools only measure the usability dimension, other tools provide information such as engagement, esthetics, or functionality. Furthermore, 2 measurement tools, Mobile App Rating Scale and mHealth Apps Usability Questionnaire, have a user version, as well as a professional version. ConclusionsThe Health Information Technology Usability Evaluation Scale and the Measurement Scales for Perceived Usefulness and Perceived Ease of Use were the most validated tools, but they were very focused on usability. The Mobile App Rating Scale showed a moderate number of validated psychometric properties, measures a significant number of quality dimensions, and has been validated in a large number of mHealth apps, and its use is widespread. It is suggested that the continuation of the validation of this tool in other psychometric properties could provide an appropriate option for evaluating the quality of mHealth apps.

Background: Sedentary behavior (SB) negatively impact health and is highly prevalent in the population. Digital behavior change interventions (DBCIs) have been developed to modify behaviors such as SB by technologies. However, it is unknown which behavior change techniques (BCTs) are most frequently employed in SB as well as the effect associated with DBCIs in this field. The aim of this systematic review was: (a) to evaluate the BCT most frequently employed in digital health including all technologies available and interventions aimed at increasing physical activity (PA), reducing sedentary time, and improving adherence to exercise in the clinical population, and (b) to review the effect associated with DBCIs in this field.Methods: The database used was Medline, as well as Scopus, Scielo, and Google Scholar. For the search strategy, we considered versions of behavior/behavioral, mHealth/eHealth/telemedicine/serious game/gamification. The terms related to PA and SB were included, the criteria for inclusion were randomized clinical trials (RCTs), adults, intervention based on digital media, and outcome variable lifestyle modification; a last 5 years filter was included. Michie's Taxonomy was used to identify BCTs. The study was registered under the number PROSPERO CRD42019138681.Results: Eighteen RCTs were included in the present systematic review, 5 of them healthy adults, and 13 of them with some illness. Studies included 2298 sedentary individuals who were followed up for 5 weeks−3 years. The most used BCTs were goal setting, problem solving, review outcomes/goals, feedback on behavior and outcomes of behavior, self-monitoring of behavior, social support, information about health consequences, and behavior practice/rehearsal. The effect associated with DBCIs showed improvements, among several related to PA and physiologic self-reported and anthropometric outcomes.Conclusion: The BCTs most used in digital health to change outcomes related to SB were goals and planning, feedback and monitoring, social support, natural consequences, repetition, and substitution. Besides these findings, DBCIs are influenced by several factors like the type of intervention, patients' preferences and values, or the number of BCTs employed. More research is needed to determine with precision which DBCIs or BCTs are the most effective to reduce SB in the clinical population.

Increased permeability of the epithelial and endothelial cell layers results in the onset of pathogenic mechanisms. In both cell types, cell–cell connections play a regulatory role in altering membrane permeability. The aim of this study was to investigate the modulating effect of anthocyanin-rich extract (AC) on TJ proteins in inflammatory Caco-2 and HUVEC monolayers. Distribution of Occludin and zonula occludens-1 (ZO-1) were investigated by immunohistochemical staining and the protein levels were measured by flow cytometry. The mRNA expression was determined by quantitative real-time PCR. The transepithelial electrical resistance (TEER) values were measured during a permeability assay on HUVEC cell culture. As a result of inflammatory induction by TNF-α, redistribution of proteins was observed in Caco-2 cell culture, which was reduced by AC treatment. In HUVEC cell culture, the decrease in protein and mRNA expression was more dominant during inflammatory induction, which was compensated for by the AC treatment. Overall, AC positively affected the expression of the examined cell-binding structures forming the membrane on both cell types.

Real-time multilingual phrase detection from/during online video presentations—to support instant remote diagnostics—requires near real-time visual (textual) object detection and preprocessing for further analysis. Connecting remote specialists and sharing specific ideas is most effective using the native language. The main objective of this paper is to analyze and propose—through DEtection TRansformer (DETR) models, architectures, hyperparameters—recommendation, and specific procedures with simplified methods to achieve reasonable accuracy to support real-time textual object detection for further analysis. The development of real-time video conference translation based on artificial intelligence supported solutions has a relevant impact in the health sector, especially on clinical practice via better video consultation (VC) or remote diagnosis. The importance of this development was augmented by the COVID-19 pandemic. The challenge of this topic is connected to the variety of languages and dialects that the involved specialists speak and that usually needs human translator proxies which can be substituted by AI-enabled technological pipelines. The sensitivity of visual textual element localization is directly connected to complexity, quality, and the variety of collected training data sets. In this research, we investigated the DETR model with several variations. The research highlights the differences of the most prominent real-time object detectors: YOLO4, DETR, and Detectron2, and brings AI-based novelty to collaborative solutions combined with OCR. The performance of the procedures was evaluated through two research phases: a 248/512 (Phase1/Phase2) record train data set, with a 55/110 set of validated data instances for 7/10 application categories and 3/3 object categories, using the same object categories for annotation. The achieved score breaks the expected values in terms of visual text detection scope, giving high detection accuracy of textual data, the mean average precision ranging from 0.4 to 0.65.

Through this study, we developed and validated a system for energy expenditure calculation, which only requires low-cost inertial sensors and open source R software. Five healthy subjects ran at ten different speeds while their kinematic variables were recorded on the thigh and wrist. Two ActiGraph wireless inertial sensors and a low-cost Bluetooth-based inertial sensor (Lis2DH12), assembled by SensorID, were used. Ten energy expenditure equations were automatically calculated in a developed open source R software (our own creation). A correlation analysis was used to compare the results of the energy expenditure equations. A high interclass correlation coefficient of estimated energy expenditure on the thigh and wrist was observed with an Actigraph and Sensor ID accelerometer; the corrected Freedson equation showed the highest values, and the Santos-Lozano vector magnitude equation and Sasaki equation demonstrated the lowest one. Energy expenditure was compared between the wrist and thigh and showed low correlation values. Despite the positive results obtained, it was necessary to design specific equations for the estimation of energy expenditure measured with inertial sensors on the thigh. The use of the same formula equation in two different placements did not report a positive interclass correlation coefficient.

Diabetes mellitus (DM)-related morbidity and mortality are steadily rising worldwide, affecting about half a billion people worldwide. A significant proportion of diabetic cases are in the elderly, which is concerning given the increasing aging population. Proper nutrition is an important component in the effective management of diabetes in the elderly. A plethora of active substances of plant origin exhibit potency to target the pathogenesis of diabetes mellitus. The nutraceutical and pharmaceutical effects of anthocyanins have been extensively studied. In this study, the effect of Hungarian sour cherry, which is rich in anthocyanins, on hyperglycemia-induced endothelial dysfunction was tested using human umbilical cord vein endothelial cells (HUVECs). HUVECs were maintained under both normoglycemic (5 mM) and hyperglycemic (30 mM) conditions with or without two concentrations (1.50 ng/µL) of anthocyanin-rich sour cherry extract. Hyperglycemia-induced oxidative stress and inflammatory response and damaged vasorelaxation processes were investigated by evaluating the level of reactive oxygen species (ROS) and gene expression of four proinflammatory cytokines, namely, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), interleukin-8 (IL-8), and interleukin-1α (IL-1α), as well as the gene expression of nitric oxide synthase (NOS) endothelin-1 (ET-1) and endothelin-converting enzyme-1 (ECE-1). It was found that hyperglycemia-induced oxidative stress was significantly suppressed by anthocyanin-rich sour cherry extract in a concentration-dependent manner. The gene expression of the tested proinflammatory cytokines increased under hyperglycemic conditions but was significantly reduced by both 1 and 50 ng/µL anthocyanin-rich sour cherry extract. Further, although increased ET-1 and ECE-1 expression due to hyperglycemia was reduced by anthocyanin-rich sour cherry extract, NOS expression was increased by the extract. Collectively, these data suggest that anthocyanin-rich sour cherry extract could alleviate hyperglycemia-induced endothelial dysfunction due to its antioxidant, anti-inflammatory, and vasorelaxant effects.

Diabetes mellitus-related morbidity and mortality is a rapidly growing healthcare problem, globally. Several nutraceuticals exhibit potency to target the pathogenesis of diabetes mellitus. The antidiabetic effects of compounds of garlic have been extensively studied, however, limited data are available on the biological effects of a certain garlic component, allithiamine. In this study, allithiamine was tested using human umbilical cord vein endothelial cells (HUVECs) as a hyperglycaemic model. HUVECs were isolated by enzymatic digestion and characterized by flow cytometric analysis using antibodies against specific marker proteins including CD31, CD45, CD54, and CD106. The non-cytotoxic concentration of allithiamine was determined based on MTT, apoptosis, and necrosis assays. Subsequently, cells were divided into three groups: incubating with M199 medium as the control; or with 30 mMol/L glucose; or with 30 mMol/L glucose plus allithiamine. The effect of allithiamine on the levels of advanced glycation end-products (AGEs), activation of NF-κB, release of pro-inflammatory cytokines including IL-6, IL-8, and TNF-α, and H2O2-induced oxidative stress was investigated. We found that in the hyperglycaemia-induced increase in the level of AGEs, pro-inflammatory changes were significantly suppressed by allithiamine. However, allithiamine could not enhance the activity of transketolase, but it exerts a potent antioxidant effect. Collectively, our data suggest that allithiamine could alleviate the hyperglycaemia-induced endothelial dysfunction due to its potent antioxidant and anti-inflammatory effect by a mechanism unrelated to the transketolase activity.

Hungarian sour cherries (SC) are excellent source of anthocyanin (concentrations (100⁻300 mg in 100 g fresh fruit) and melatonin (0.15 mg in 100 g fresh fruit), but other flavonoid derivatives also can be isolated by aqueous alcoholic extraction. We have developed a new process for extracting non-extractable procyanidines bound to the membrane, proteins, and fibers. These compounds were seperated with UHPLC-MS methods, and the structure of individual components were identified on the basis of their mass fragmentation spectra. The antioxidant capacity of soluble and non-soluble antioxidants were measured with ferric reducing antioxidant power (FRAP), 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity (DPPH), trolox equivalent antioxidant capacity (TEAC) assays, and compared to the new measurement methods of water-soluble antioxidant capacity (ACW), lipid-soluble antioxidant capacity (ACL). Furthermore, total phenolic content (TPC) and total procyanidin content (PAC) were determinated. As a result of our investigation, we found that the solvent combination, where in the first step is water⁻ethanol (1:1), then 100% ethanol were suitable for the extraction of the extractable antioxidants. However, the chemiluminescence method that is based on the elimination of the superoxide radical is more accurate than other colorimetric methods which measure antioxidant capacity.

A natural fat-soluble thiamine derivative, namely N-[(4-amino-2-methylpyrimidin-5-yl)methyl]-N-[(2E)-5-hydroxy-3-(prop-2-en-1-yldisulfanyl)pent-2-en-2-yl]formamide (allithiamine) has been identified only in garlic (Allium sativum) until now. Hungarian red sweet pepper (Capsicum annuum) was found as a new source of allithiamine. Extraction procedure and analytical method were developed for the isolation of allithiamine and a chemical synthesis of the compound was also developed. First solid-liquid extraction was performed with 96 % ethanol to isolate allithiamine from pepper seeds. Thereafter, solid phase extraction was applied from ethanolic extract using C18 cartridge to concentrate and purify samples for further analysis. The structure of the synthesized and the isolated compounds was verified by reverse phase HPLC, HPLC-MS, MALD-TOF MS and NMR. Furthermore, effect of allithiamine was investigated on streptozotocin-induced diabetic mice with neuropathy. The results show that neuropathic pain sensation is improved by allithiamine treatment similarly to benfothiamine.

The anthocyanin content of Hungarian sour cherry is remarkable based on our preliminary investigations. Nutraceutical and pharmaceutical effects of anthocyanins have been extensively studied. The objective of this work was to investigate the the effect of purified sour cherry extract using human umbilical cord vein endothelial cells (HUVECs) as the inflammatory model. HUVECs were isolated by enzymatic digestion and characterized by flow cytometry. The optimal concentration range of sour cherry extract was selected based on MTT, apoptosis, and necrosis assays. Cells were divided into three groups, incubating with M199 medium as control, or with lipopolysaccharide (LPS) or with LPS plus anthocyanin extract (ACE). The effect of sour cherry extract on oxidative stress, pro-inflammatory factors, and arachidonic pathway was investigated. An amount of 50 μg/mL ACE (ACE50) was able to increase the level of glutathione and decrease the ROS, thereby improving the unbalanced redox status in inflammation. ACE50 lowered pro-inflammatory cytokine levels including Interleukin-6 (IL-6), regulated on activation, normal T cell expressed and secreted (RANTES), granulocyte-macrophage colony-stimulating factor (GM-CSF), and tumor necrosis factor alpha (TNF-α). ACE50 affected the arachidonic acid pathway by reducing the LPS-induced enzyme expression (cyclooxygenase-1, cyclooxygenase-2, and prostacyclin synthase). The extract under investigation seems to have a pleiotropic effect including anti-oxidative, anti-inflammatory, hemostatic, and vasoactive effects. Our results indicate that purified sour cherry extract could reduce the LPS-induced inflammatory response, thereby improving endothelial dysfunction.