DIETOS: A dietary recommender system for chronic diseases monitoring and management

Health-aware food recommendation system with dual attention in heterogeneous graphs

Recommender systems (RS) have been increasingly applied to food and health. However, challenges still remain, including the effective incorporation of heterogeneous information and the discovery of meaningful relationships among entities in the context of food and health recommendations. To address these challenges, we propose a novel framework, the Health-aware Food Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA), for unsupervised representation learning on heterogeneous graph-structured data. HFRS-DA utilizes an attention technique to reconstruct node features and edges and employs a dual hierarchical attention mechanism for enhanced unsupervised learning of attributed graph representations. HFRS-DA addresses the challenge of effectively leveraging the heterogeneous information in the graph and discovering meaningful semantic relationships between entities. The framework analyses recipe components and their neighbours in the heterogeneous graph and can discover popular and healthy recipes, thereby promoting healthy eating habits. We compare HFRS-DA using the Allrecipes dataset and find that it outperforms all the related methods from the literature. Our study demonstrates that HFRS-DA enhances the unsupervised learning of attributed graph representations, which is important in scenarios where labelled data is scarce or unavailable. HFRS-DA can generate node embeddings for unused data effectively, enabling both inductive and transductive learning.

A fuzzy based dietary clinical decision support system for patients with multiple chronic conditions (MCCs)

Due to the multifaceted nature of Multiple Chronic Conditions (MCCs), setting a diet for these patients is complicated and time-consuming. In this study, a clinical decision support system based on fuzzy logic was modeled and evaluated to aid dietitians in adjusting the diet for patients with MCCs. Mamdani fuzzy logic with 1144 rules was applied to design the model for MCCs patients over 18 years who suffer from one or more chronic diseases, including obesity, diabetes, hypertension, hyperlipidemia, and kidney disease. One hundred nutrition records from three nutrition clinics were employed to measure the system's performance. The findings showed that the diet set by nutritionists had no statistically significant difference from the diet recommended by the fuzzy model (p > 0.05), and there was a strong correlation close to one between them. In addition, the results indicated a suitable model performance with an accuracy of about 97%. This system could adjust the diet with high accuracy as well as humans. In addition, it could increase dietitians' confidence, precision, and speed in setting the diet for MCCs patients.

Symbolic knowledge extraction for explainable nutritional recommenders

BACKGROUND AND OBJECTIVE

This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts' prescriptions, (ii) adherence to users' tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional RS, combining machine learning and symbolic knowledge extraction to profile users-hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts' prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations. ConclusionsThanks to our approach, intelligent agents may learn users' preferences from data, convert them into symbolic form, and extend them with experts' goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective, while the whole process of recommendation generation is made explainable.

Health-Aware Food Recommendation Based on Knowledge Graph and Multi-Task Learning

Current food recommender systems tend to prioritize either the user's dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account the user's personalized health requirements, in addition to their dietary preferences. Our work comprises three perspectives. Firstly, we propose a collaborative recipe knowledge graph (CRKG) with millions of triplets, containing user-recipe interactions, recipe-ingredient associations, and other food-related information. Secondly, we define a score-based method for evaluating the healthiness match between recipes and user preferences. Based on these two prior perspectives, we develop a novel health-aware food recommendation model (FKGM) using knowledge graph embedding and multi-task learning. FKGM employs a knowledge-aware attention graph convolutional neural network to capture the semantic associations between users and recipes on the collaborative knowledge graph and learns the user's requirements in both preference and health by fusing the losses of these two learning tasks. We conducted experiments to demonstrate that FKGM outperformed four competing baseline models in integrating users' dietary preferences and personalized health requirements in food recommendations and performed best on the health task.

A Systematic Review on Food Recommender Systems for Diabetic Patients

Recommender systems are currently a relevant tool for facilitating access for online users, to information items in search spaces overloaded with possible options. With this goal in mind, they have been used in diverse domains such as e-commerce, e-learning, e-tourism, e-health, etc. Specifically, in the case of the e-health scenario, the computer science community has been focused on building recommender systems tools for supporting personalized nutrition by delivering user-tailored foods and menu recommendations, incorporating the health-aware dimension to a larger or lesser extent. However, it has been also identified the lack of a comprehensive analysis of the recent advances specifically focused on food recommendations for the domain of diabetic patients. This topic is particularly relevant, considering that in 2021 it was estimated that 537 million adults were living with diabetes, being unhealthy diets a major risk factor that leads to such an issue. This paper is centered on presenting a survey of food recommender systems for diabetic patients, supported by the PRISMA 2020 framework, and focused on characterizing the strengths and weaknesses of the research developed in this direction. The paper also introduces future directions that can be followed in the next future, for guaranteeing progress in this necessary research area.

Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping

BACKGROUND

Health recommender systems (HRSs) are information retrieval systems that provide users with relevant items according to the users' needs, which can motivate and engage users to change their behavior.

OBJECTIVE

This study aimed to identify the development and evaluation of HRSs and create an evidence map.

METHODS

A total of 6 databases were searched to identify HRSs reported in studies from inception up to June 30, 2022, followed by forward citation and grey literature searches. Titles, abstracts, and full texts were screened independently by 2 reviewers, with discrepancies resolved by a third reviewer, when necessary. Data extraction was performed by one reviewer and checked by a second reviewer. This review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement.

RESULTS

A total of 51 studies were included for data extraction. Recommender systems were used across different health domains, such as general health promotion, lifestyle, and generic health service. A total of 23 studies had reported the use of a combination of recommender techniques, classified as hybrid recommender systems, which are the most commonly used recommender techniques in HRSs. In the HRS design stage, only 10 of 51 (19.6%) recommender systems considered personal preferences of end users in the design or development of the system; a total of 29 studies reported the user interface of HRSs, and most HRSs worked on users' mobile interfaces, usually a mobile app. Two categories of HRS evaluations were used, and evaluations of HRSs varied greatly; 62.7% (32/51) of the studies used the offline evaluations using computational methods (no user), and 33.3% (17/51) of the studies included end users in their HRS evaluation.

CONCLUSIONS

Through this scoping review, nonmedical professionals and policy makers can visualize and better understand HRSs for future studies. The health care professionals and the end users should be encouraged to participate in the future design and development of HRSs to optimize their utility and successful implementation. Detailed evaluations of HRSs in a user-centered approach are needed in future studies.

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

A health recommender system (HRS) provides a user with personalized medical information based on the user's health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management.

Using Popular Foods Consumed to Inform Development of Digital Tools for Dietary Assessment and Monitoring

Knowing the type and quality of the most popular foods consumed by a population can be useful in the design of technologies for monitoring food intake and interventions. The aim of this research was to determine the most frequently consumed foods and beverages among the Australian population and provide recommendations for progressing the design of dietary assessment technologies. Analysis of the first 24 h recall of the most recent Australian National Nutrition and Physical Activity Survey was conducted. The most popular foods and beverages consumed by energy (kJ) and by frequency were calculated. There were 4515 separate foods and beverages reported by 12,153 people. Overall, the top 10 foods that contributed most energy included full fat milk, beer, white rice, white bread, red wine, cola soft drinks, bananas, red apples, wholewheat breakfast cereal and white sugar. The five most frequently reported foods and beverages were tap water, black tea, full fat milk, instant coffee, and sugar. Understanding the most popular foods and beverages consumed can support innovations in the design of digital tools for dietary surveillance and to reduce under-reporting and food omissions. These findings could also guide the development of more tailored and relevant food databases that underpin these technologies.

The Benefits of Crowdsourcing to Seed and Align an Algorithm in an mHealth Intervention for African American and Hispanic Adults: Survey Study

BACKGROUND

The lack of publicly available and culturally relevant data sets on African American and bilingual/Spanish-speaking Hispanic adults' disease prevention and health promotion priorities presents a major challenge for researchers and developers who want to create and test personalized tools built on and aligned with those priorities. Personalization depends on prediction and performance data. A recommender system (RecSys) could predict the most culturally and personally relevant preventative health information and serve it to African American and Hispanic users via a novel smartphone app. However, early in a user's experience, a RecSys can face the "cold start problem" of serving untailored and irrelevant content before it learns user preferences. For underserved African American and Hispanic populations, who are consistently being served health content targeted toward the White majority, the cold start problem can become an example of algorithmic bias. To avoid this, a RecSys needs population-appropriate seed data aligned with the app's purposes. Crowdsourcing provides a means to generate population-appropriate seed data.

OBJECTIVE

Our objective was to identify and test a method to address the lack of culturally specific preventative personal health data and sidestep the type of algorithmic bias inherent in a RecSys not trained in the population of focus. We did this by collecting a large amount of data quickly and at low cost from members of the population of focus, thereby generating a novel data set based on prevention-focused, population-relevant health goals. We seeded our RecSys with data collected anonymously from self-identified Hispanic and self-identified non-Hispanic African American/Black adult respondents, using Amazon Mechanical Turk (MTurk).

METHODS

MTurk provided the crowdsourcing platform for a web-based survey in which respondents completed a personal profile and a health information-seeking assessment, and provided data on family health history and personal health history. Respondents then selected their top 3 health goals related to preventable health conditions, and for each goal, reviewed and rated the top 3 information returns by importance, personal utility, whether the item should be added to their personal health library, and their satisfaction with the quality of the information returned. This paper reports the article ratings because our intent was to assess the benefits of crowdsourcing to seed a RecSys. The analysis of the data from health goals will be reported in future papers.

RESULTS

The MTurk crowdsourcing approach generated 985 valid responses from 485 (49%) self-identified Hispanic and 500 (51%) self-identified non-Hispanic African American adults over the course of only 64 days at a cost of US $6.74 per respondent. Respondents rated 92 unique articles to inform the RecSys.

CONCLUSIONS

Researchers have options such as MTurk as a quick, low-cost means to avoid the cold start problem for algorithms and to sidestep bias and low relevance for an intended population of app users. Seeding a RecSys with responses from people like the intended users allows for the development of a digital health tool that can recommend information to users based on similar demography, health goals, and health history. This approach minimizes the potential, initial gaps in algorithm performance; allows for quicker algorithm refinement in use; and may deliver a better user experience to individuals seeking preventative health information to improve health and achieve health goals.

A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes

Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management; however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake. In this paper, we propose, develop, and implement a nudge-inspired artificial intelligence (AI)-driven health platform for self-management of diabetes. The proposed platform has been co-designed with patients and clinicians, using the adapted 4-cycle design science research methodology (A4C-DSRM) model. The platform includes (a) a cross-platform mobile application for patients that incorporates a macronutrient detection algorithm for meal recognition and nudge-inspired meal logger, and (b) a web-based application for the clinician to support the self-management regime of patients. Further, the platform incorporates behavioral intervention techniques stemming from nudge theory that aim to support and encourage a sustained change in patient lifestyle. Application of the platform has been demonstrated through an illustrative case study via two exemplars. Further, a technical evaluation is conducted to understand the performance of the MDA to meet the personalization requirements of patients with type II diabetes.

Driving Management of Novel Foods: A Network Analysis Approach

The food industry has confronted, in recent years, numerous issues including meeting a food demand for individual well-being in a sufficient and healthy manner, also due to the effects of the world population growth. In this scenario, alternative food sources may be a key element both for their contribution to food needs and for the promotion of sustainable and innovative production patterns. These food sources, new compared to traditional food styles, have been regulated by specific European Union regulations under the definition of novel foods. Their importance in the world has raised different topics of scientific research. The present paper aimed to seize the direction of scientific studies in the world focused on the thematic area of novel foods, from a management point of view. This study analyzed 209 papers and carried out a descriptive analysis and a network analysis of the thematic areas under examination also with the help of the software VOSviewer. The results highlighted the importance of scientific research in the world also for the contributions on the exploration of existing markets as well as for the innovative solutions it provides, which aim to expand market possibilities. Finally, the existence of several elements and factors, which may discourage the propensity to consume and therefore the development of the novel foods market, seemed to emerge, and for this reason, many surveys focused on finding solutions to overcome these potential obstacles.

Designing Persuasive Food Conversational Recommender Systems With Nudging and Socially-Aware Conversational Strategies

Unhealthy eating behavior is a major public health issue with serious repercussions on an individual’s health. One potential solution to overcome this problem, and help people change their eating behavior, is to develop conversational systems able to recommend healthy recipes. One challenge for such systems is to deliver personalized recommendations matching users’ needs and preferences. Beyond the intrinsic quality of the recommendation itself, various factors might also influence users’ perception of a recommendation. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users’ eating habits and current preferences. Users can interact with Cora in two different ways. They can select pre-defined answers by clicking on buttons to talk to Cora or write text in natural language. Additionally, Cora can engage users through a social dialogue, or go straight to the point. Cora is also able to propose different alternatives and to justify its recipes recommendation by explaining the trade-off between them. We conduct two experiments. In the first one, we evaluate the impact of Cora’s conversational skills and users’ interaction mode on users’ perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users’ perception of the interaction as well as their perception of the system. In the second evaluation, we evaluate the influence of Cora’s explanations and recommendation comparisons on users’ perception. Our results show that explanations positively influence users’ perception of a recommender system. However, comparing healthy recipes with a decoy is a double-edged sword. Although such comparison is perceived as significantly more useful compared to one single healthy recommendation, explaining the difference between the decoy and the healthy recipe would actually make people less likely to use the system.

Using dual-network-analyser for communities detecting in dual networks

BACKGROUND

Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges.

RESULTS

We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data.

CONCLUSION

The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs.

Prevention and control of urinary tract stones using a smartphone-based self-care application: design and evaluation

BACKGROUND

Self-care and participation of patients in improving health and increasing awareness about the risk factors that affect the development of disease in patients with urinary tract stones are influential factors in controlling and improving the quality of life in these patients. In this regard, the availability and capability of smartphones increase patients' self-care ability. The present study aimed to develop and evaluate a self-care application based on smartphones for patients with urinary tract stones.

METHODS

The present study is a developmental and applied study that was conducted in three phases. First, the information needs and functionalities of the self-care application were determined by surveying 101 patients, 32 urologists and nephrologists, 11 nurses, and six other specialists. In the second phase, the initial sample of the smartphone-based application was created, and in the third phase, the designed application was evaluated by 15 experts using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and Nielsen's Attributes of Usability (NAU) questionnaire. Results of the questionnaires were entered into SPSS-23 software for analysis using descriptive statistics.

RESULTS

In the first phase, 21 information elements and nine critical functionalities for the self-care application were identified, and then this application was designed by Java programming language. The evaluation of experts showed that two aspects of the quality of system user interface from the user's point of view and the overall performance of the application together obtained the highest score (6.43 from 7), which was equal to 91.85%. Then according to the experts, aspects of the degree of convenience and user-friendliness of the application received the highest score (6.10 from 7), which was equal to 87.14%, and also all aspects of the application were evaluated at an acceptable level. In general, results of the evaluation of application's usability by experts showed that the usability of the application for patients with urinary tract stones was at an acceptable level.

CONCLUSION

According to the results obtained from evaluating the smartphone-based application for patients with urinary tract stones, this self-care application can be used to prevent and control urinary tract stones and facilitate self-care and active patient participation in care.

Health Recommender Systems: Systematic Review

Background

Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior.

Objective

We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations.

Methods

We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review.

Results

Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used.

Conclusions

There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

A minimum data set of user profile or electronic health record for chemical warfare victims' recommender system

Background:

There are many people who are suffering from a variety of physical and mental illnesses due to the chemical attacks. There are various technologies such as recommender systems that can identify the main concerns related to health and make efforts to address them. To design and develop a recommender system, preparation of data source of this system should be considered. The aim of this study was to determine the minimum data set for user profile or user's electronic health record in chemical warfare victims’ recommender system.

Methods:

This applied descriptive, cross-sectional study which was conducted in 2017. A questionnaire was developed by the authors from the data elements that were collected using the data extraction form from the studied sources. Content validity of the questionnaire was confirmed by using the experts. Test–retest method was used to determine the reliability of the questionnaire. The reliability of the questionnaire with Cronbach's alpha coefficient was confirmed as 84%. The questionnaire were submitted for related experts based on Delphi method by email or in person. Data resulting from the Delphi technique with descriptive statistics methods in SPSS software were analyzed.

Results:

Forty-seven nonclinical data elements and 181 clinical data elements were classified.

Conclusion:

Determining minimum data set of user profile or electronic health record in the recommender system for chemical warfare victims helps the health authorities to implement the recommender system which demonstrates chemical warfare victims’ needs.

Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System

BACKGROUND

Health education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds.

OBJECTIVE

The aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system.

METHODS

A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP).

RESULTS

The constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628.

CONCLUSIONS

This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.

Atherosclerotic-nephropathy: an updated narrative review

The increased prevalence of chronic kidney disease (CKD) in elderly patients recognizes, as main cause, the long-term exposure to atherosclerosis and hypertension. Chronic ischemic damage due to critical renal arterial stenosis induces oxidative stress and intra-renal inflammation, resulting in fibrosis and microvascular remodelling, that is the histological picture of atherosclerotic renal vascular disease (ARVD). The concomitant presence of a long history of hypertension may generate intimal thickening and luminal narrowing of renal arteries and arterioles, glomerulosclerosis, interstitial fibrosis and tubular atrophy, more typically expression of hypertensive nephropathy. These complex mechanisms contribute to the development of CKD and the progression to End Stage Kidney Disease. In elderly CKD patients, the distinction among these nephropathies may be problematic; therefore, ischemic and hypertensive nephropathies can be joined in a unique clinical syndrome defined as atherosclerotic nephropathy. The availability of novel diagnostic procedures, such as intra-vascular ultrasound and BOLD-MRI, in addition to traditional imaging, have opened new scenarios, because these tools allow to identify ischemic lesions responsive to renal revascularization. Indeed, although trials have deflated the role of renal revascularization on the renal outcomes, it should be still used to avoid dialysis initiation and/or to reduce blood pressure in selected elderly patients at high risk. Nonetheless, lifestyle modifications (smoking cessation, increased physical activity), statins and antiplatelet use, as well as cautious use of renin-angiotensin system inhibitors, remain the main therapeutic approach aimed at slowing the renal damage progression. Mesenchymal stem cells and Micro-RNA are promising target of anti-fibrotic therapy, which might provide potential benefit in ARVD patients, though safety and efficacy profile in humans is unknown too.

Development and Validation of an Application for Follow-up of Patients Undergoing Dialysis: NefroPortátil

OBJECTIVES

To develop the NefroPortatil mobile application (app) and evaluate its effects on the management of patients undergoing dialysis.

METHODS

The first stage of the work was the development, installation, and establishment of the instructions to use the phone app as an instrument to aid in the control of fluid and food intake of 52 patients undergoing dialysis. In the second stage, the patients were monitored for 3 months and evaluated using questionnaires to measure the improvement in quality of life (Kidney Disease Quality of Life Instrument) and self-management of disease (Perceived Medical Condition Self-Management Scale) by the app. In addition, laboratory tests were performed before app use and in the first, second, and third months of its use (January to April 2018). Analysis of variance was used to analyze the laboratory data, and a paired Student's t test was used to analyze the responses to the questionnaires and as a posttest (P < .05).

RESULTS

Among the laboratory test results, serum phosphorus levels showed a significant difference (P < .04) after the app was used. A significant improvement was observed in self-management of the disease according to the Perceived Medical Condition Self-Management Scale questionnaire (P < .03). The usability of the app reached a median score of 9.65 from a total score of 10.

CONCLUSION

The NefroPortatil app improved the degree of perception of self-care of patients undergoing dialysis with chronic kidney failure, in addition to favoring nutritional control.

Knowledge Management in Healthcare Sustainability: A Smart Healthy Diet Assistant in Traditional Chinese Medicine Culture

In the past 40 years, with the changes to dietary structure and the dramatic increase in the consumption of meat products in developing countries, especially in China, encouraging populations to maintain their previous healthy eating patterns will have health, environmental, and economic co-benefits. Healthy diet education plays an important role in the promotion of people’s healthy behavior. However, in the modern age, the data regarding healthy diets available on the internet is increasing rapidly and is distributed on multiple sources. It is time-consuming for users to learn about healthy diets on the internet: they need to search data on multiple platforms, choose and integrate information, and then understand what they have learned. To help people retrieve and learn healthy diet knowledge more efficiently and comprehensively, this paper designs a knowledge graph to integrate healthy diet information on the internet and provides a semantic retrieval system. In the knowledge graph, five main concepts are defined, including food material, dish, nutritional element, symptom, and crowd, as well as the relationships among them. In addition, Chinese dietary culture elements and traditional Chinese medicine (TCM) theory are also contained in the knowledge graph. The preliminary results show that by using the system, users learn healthy diet knowledge more quickly and comprehensively and they are more inclined to have balanced diets. This work could be regarded as a retrieval and education tool, which can assist healthcare and national sustainable development.

An open-label, randomized, placebo-controlled study on the effectiveness of a novel probiotics administration protocol (ProbiotiCKD) in patients with mild renal insufficiency (stage 3a of CKD)

Purpose

Gut dysbiosis has been described in advanced, but not in initial stages of CKD. Considering the relevant impact of gut dysbiosis on renal and cardiovascular risk, its diagnosis and treatment are clinically relevant.

Methods

We designed, open-label, placebo-controlled intervention study (ProbiotiCKD) to evaluate gut microbiota metabolism in a cohort of KDIGO CKD patients (n = 28) at baseline and after a randomly assigned treatment with probiotics or placebo. Gut microbiota status was evaluated on:.

Results

Basal mean fecal Lactobacillales and Bifidobacteria concentrations were abnormally low in both groups, while urinary indican and 3-MI levels were, indicating a mixed (fermentative and putrefactive) dysbiosis. After treatment, mean fecal Lactobacillales and Bifidobacteria concentrations were increased, only in the probiotics group (p < 0.001). Conversely, mean urinary indican and 3-MI levels only in the group treated with probiotics (p < 0.001). Compared to placebo group, significant improvements of C-reactive protein (p < 0.001), iron (p < 0.001), ferritin (p < 0.001), transferrin saturation (p < 0.001), β2-microglobulin (p < 0.001), serum iPTH and serum calcium were observed only in the probiotics group.

Conclusions

ProbiotiCKD is the first intervention study demonstrating that an intestinal mixed dysbiosis is present even in early CKD stage and can be effectively corrected by the novel mode of administration of high-quality probiotics with improvement of inflammatory indices, iron status and iPTH stabilization.