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Abnormal thyroid hormone (TH) function has been observed in all components of metabolic syndrome (MetS), but the mechanisms remain unclear. Altered genomic methylation status is closely related to MetS. Our aim was to determine whether methylation regulation in TH function–related genes is involved in MetS. In a small strictly selected cohort, low TH function was observed in MetS group, as well as lower THRB promoter methylation levels in peripheral blood leukocytes in a genome-wide methylation screening by Illumina 450K beadchip. The results of beadchip assay were then confirmed by Sequenom MassARRAY. Low THRB promoter methylation levels and low TH function in MetS were confirmed in another big-size validation cohort. Lower methylation levels were associated with higher THRB expression in peripheral blood leukocytes, and altered THRB promoter methylation status influenced its promoter activity and expression. In the MetS rat models constructed by high fat and high fructose diet, lower TH function was also observed, as well as lower Thrb promoter methylation levels. Furthermore, systematic inflammation observed in MetS was found to induce decreased THRB promoter methylation levels as well as corresponding THRB expression. Additionally, oral treatment with a physiological T3 dose mitigated hypertension and insulin resistance and partially alleviated hepatic steatosis and adipocyte hypertrophy in MetS rats. Low methylation levels of THRB promoter in peripheral blood leukocytes induced by systematic inflammation were involved in low TH function in MetS, whereas low TH function deteriorates MetS. This might serve as a novel therapeutic target of MetS.

Exploitation of telehealth in prenatal care has the potential to reduce the access barrier to care and empower women to participate in their own care. This review aims to assess the practical implications of virtual prenatal care and identify the needs and experiences associated with it.A systematic literature review was conducted in four electronic databases: PubMed, Web of Science, Scopus, and Cochrane. The keywords used were "pregnancy", "virtual visit", "prenatal", and others. The search included all relevant studies published from 2011 to 2021 written in English. Articles mentioning virtual prenatal care incorporating synchronous communication between pregnant women and health care professionals were included. Those unrelated to prenatal care or employing asynchronous means of virtual care were excluded. The review was structured following the PRISMA guidelines. Different quality appraisal methods such as JBI, CASP, NOS, and Cochrane were used to assess the methodological quality of the literature. The data were then analyzed based on the categorization of the studies.Overall, 2863 articles were identified, of which 19 met the inclusion criteria after removing duplicates, screening of abstracts, and full text-four articles identified from hand-searching were incorporated, making a total of 23 eligible articles for the review. The studies' findings revealed the preference for implementing cost-effective virtual care based on the resource set, technological literacy, and consistent accessibility. Further, no significant differences in clinical outcomes were observed between two modes of care, virtual and in-person. The higher satisfaction by pregnant women and healthcare professionals indicated the continuity of the care. In addition, the hybrid model of virtual prenatal care integrated with traditional in-person care was acceptable to both low-risk and high-risk pregnant women. Virtual prenatal care substantially reduced travel time and absences from work, drops in clinic wait time and no-show rate, limited the risk of exposure during a pandemic, and increased self-accountability.Virtual prenatal care offers predominant advantages over in-person when it is carefully designed with the inclusion of pregnant women and healthcare professionals' needs. Evidence showed that providing adequate technology training, proper instruction, and guidelines for initial setup and assurance of a reliable and accessible system is vital in increasing access to care.

BACKGROUND Regular physical activity (PA), healthy habits, and a healthy diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process. OBJECTIVE This study proposes a user-centered design and development of an eCoach App (i.e., ProHealth eCoach) to promote a healthy lifestyle with personalized activity recommendations based on an iterative user-centered design (UCD) method. The objective is to explore the user needs for an eCoach system to stimulate a healthy lifestyle. The foremost principle of this ProHealth eCoach system is to reinforce positive behavior by persuasive strategies, such as self-monitoring of behavior, self-management, personalization, goal setting, reminder, rewards, tailored recommendation generation, and its effective presentation. Based on insights from a series of UCD workshops, we envisage PA as the basis for a healthy lifestyle and the development of the ProHealth eCoach system. However, regardless of the design and development of multiple PA apps in the Appstore and Playstore, it remains unclear how to design and develop an engaging and effective PA coaching app. METHODS We used an iterative UCD approach with a dialogue-labs method to collect qualitative data from different users (i.e., researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users) to develop a roadmap for self-management with eCoaching. We designed and developed the eCoach prototype in two stages, adopting different phases of the UCD approach and co-design events. In UCD workshop 1, we focused on identifying end-users, understanding the user’s context, specifying user requirements, designing and developing an initial low-fidelity eCoach prototype. In UCD workshop 2, we focused on maturing the low-fidelity solution design and development for the visualization of continuous and discrete data, artificial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals. RESULTS The UCD approach helped to develop a working prototype of eCoach system that meets end-user's requirements and expectations towards an effective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users. CONCLUSIONS The adopted UCD approach brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working research prototype to improve the fit between technology, end-user, and researchers. CLINICALTRIAL Not applicable.

Background: Cardiac ryanodine receptor 2 (RyR2) dysfunction and elevated diastolic Ca2+ leak have been linked to arrhythmogenesis not only in inherited arrhythmia syndromes but also in acquired forms of heart disease including heart failure (HF) and atrial fibrillation (AF). Thus, stabilizing RyR2 may exert therapeutic effects in these conditions. Objective: The purpose of this study was to investigate the effects of stabilizing RyR2 with chronic dantrolene treatment on HF development and AF inducibility in a myocardial infarction (MI)–induced HF model in rats. Methods: MI was induced in adult Sprague-Dawley rats by ligation of the left anterior descending coronary artery. Two weeks after MI surgery, rats with large MI (≥40%) were randomly assigned to MI-vehicle (n = 14) or MI-dantrolene (10 mg/kg/d; n = 13) groups. Sham-surgery rats (n = 7) served as controls. Results: Compared to the MI-vehicle group, 4-week dantrolene treatment significantly improved cardiac function, with increased left ventricular (LV) fractional shortening (19.48% ± 3.61% vs 15.43% ± 2.65%; P

Zusammenfassung Hintergrund Vor 2 Jahrzehnten wurde die Single-Incision-Chirurgie als neues Konzept in der minimalinvasiven Chirurgie etabliert. Die Cholezystektomie ist die am häufigsten durchgeführte Prozedur in dieser Technik. Die meisten Erkenntnisse beruhen auf randomisierten Studien. Es existieren keine groß angelegten multizentrischen Datenanalysen aus der klinischen Routine. Diese Analyse der klinischen Versorgungsforschung basiert auf der SILAP-Studie („single-incision multiport/single port laparoscopic abdominal surgery“). Patienten und Methode Die vorliegende Registerauswertung basiert auf Daten von 47 Kliniken im Zeitraum 2012 bis 2014. Die primären Endpunkte waren Gesamtmorbidität/Letalität. Multiple lineare und logistische Regressionsanalysen wurden durchgeführt. Die statistische Signifikanz war angegeben mit p Ergebnisse In der SILAP-Studie wurden die Daten von 975 Patienten mit Single Incision laparoscopic Cholecystectomy (SILC) in der klinischen Routine in einem Register erfasst. Die intraoperativen Komplikationen betrugen 3,2%. Die Rate der Gallengangverletzungen lag bei 0,1%. Postoperative Komplikationen traten in 3,7% der Fälle auf. Die Letalität war bei 0,2%. Die Operationszeit (Median) fiel im Studienverlauf von 60,0 auf 51,5 min (p = 0,001). Ein Zusatztrokar war in 10,3% der Fälle erforderlich. Die Konversionsrate zur konventionellen Cholezystektomie betrug 0,7%. In der multivariaten Analyse zeigten der Body-Mass-Index (p = 0,024), das männliche Geschlecht (p = 0,012) und die Operationszeit (p Schlussfolgerung Die Registeranalyse dieser prospektiv multizentrisch erhobenen Daten zeigen, dass auch außerhalb der Selektionskriterien von randomisierten Studien die SILC in der klinischen Routine sicher durchführbar ist. Einzige Einschränkung ist ein BMI > 30 kg/m2, der einen signifikanten Einfluss auf die intraoperative Rate von Minorkomplikationen hatte.

[Figure: see text].

Background: Lifestyle diseases are the leading cause of death worldwide. The gradual increase of negative behavior in humans because of physical inactivity, unhealthy habit, and improper nutrition expedites the growth of lifestyle diseases. Proper lifestyle management in the obesity context may help to reach personal weight goal or maintain a normal weight range with optimization of health behaviors (physical activity, diet, and habits). Objective: In this study, we develop a mathematical model to analyze the impact of regular physical activity, a proper diet, and healthy habits on weight change, targeting obesity as a study case. Followed by, we design an algorithm to verify our proposed model with simulated data and compare it with related proven models based on the defined constrains. Methods: We proposed a weight-change mathematical model as a function of activity, habit, and nutrition with the first law of thermodynamics, basal metabolic rate ( BMR ), total daily energy expenditure ( TDEE ), and body-mass-index ( BMI ) to establish a relationship between health behavior and weight change. Followed by, we verify the model with simulated data and compared it with related established models. In this study, we have used revised Harris-Benedict equations (HB) for BMR and TDEE calculation. Results: The proposed mathematical model showed a strong relationship between health behavior and weight change. We verified the mathematical model with a proposed algorithm using simulated data with defined constraints. The adoption of BMR and TDEE calculation following revised Harris-Benedict equations has beaten the classical Wishnofsky’s rule (3500 cal. ≈ 1 lb.) , and the models proposed by Toumasis et al., Azzeh et. Al., and Mickens et. al. with a standard deviation of ±1.829, ±2.006, ±1.85, and ±1.80, respectively. Conclusions: This study helped us to understand the impact of healthy behavior on weight change with mathematical implications and the importance of a healthy lifestyle. As a future research scope, we wish to use this model in a health eCoach system to generate personalized lifestyle recommendations to optimize health behaviors to accomplish personal weight goals.

Thyroid hormones have long been known to have a range of effects on the cardiovascular system. However, significant knowledge gaps exist concerning the precise molecular and biochemical mechanisms governing these effects and the optimal strategies for management of abnormalities in thyroid function in patients with and without preexisting cardiovascular disease. In September 2017, the National Heart, Lung, and Blood Institute convened a Working Group with the goal of developing priorities for future scientific research relating thyroid dysfunction to the progression of cardiovascular disease. The Working Group reviewed and discussed the roles of normal thyroid physiology, the consequences of thyroid dysfunction, and the effects of therapy in 3 cardiovascular areas: cardiac electrophysiology and arrhythmias, the vasculature and atherosclerosis, and the myocardium and heart failure. This report describes the current state of the field, outlines barriers and challenges to progress, and proposes research opportunities to advance the field, including strategies for leveraging novel approaches using omics and big data. The Working Group recommended research in 3 broad areas: (1) investigation into the fundamental biology relating thyroid dysfunction to the development of cardiovascular disease and into the identification of novel biomarkers of thyroid hormone action in cardiovascular tissues; (2) studies that define subgroups of patients with thyroid dysfunction amenable to specific preventive strategies and interventional therapies related to cardiovascular disease; and (3) clinical trials focused on improvement in cardiovascular performance and cardiovascular outcomes through treatment with thyroid hormone or thyromimetic drugs.

Overconsumption of a diet rich in fat and carbohydrates, called the Western diet, is a major contributor to the global epidemic of cardiovascular disease. Despite previously documented cardiovascular protection exhibited in female rats, this safeguard may be lost under certain metabolic stressors. We hypothesized that female Wistar rats challenged by a Western diet composed of 21% fat and 50% carbohydrate (34.1% sucrose) for 17 wk would develop endothelial dysfunction via endothelial Toll-like receptor 4 (TLR4) signaling. Western diet-fed female rats exhibited dysregulation of metabolism, revealing increased body weight and abdominal fat, decreased expression of adiponectin in white adipose tissue, glucose intolerance, and impaired insulin sensitivity. Western diet exposure increased hepatic triglycerides and cholesterol alongside hepatic steatosis, categorizing nonalcoholic fatty liver disease. Moreover, a Western diet negatively affected vascular function, revealing hypertension, impaired endothelium-dependent vasorelaxation, aortic remodeling, and increased reactive oxygen species (ROS) production. Aortic protein expression of TLR4 and its downstream proteins were markedly increased in the Western diet-fed group in association with elevated serum levels of free fatty acids. In vitro experiments were conducted to test whether free fatty acids contribute to vascular ROS overproduction via the TLR4 signaling pathway. Cultured endothelial cells were stimulated with palmitate in the presence of TAK-242, a TLR4 signaling inhibitor. Palmitate-induced overgeneration of ROS in endothelial cells was abolished in the presence of TAK-242. Our data show that a Western diet induced endothelial dysfunction in female rats and suggest that endothelial TLR4 signaling may play a key role in abolishing female cardiovascular protection. NEW & NOTEWORTHY A Western diet induced elevated levels of free fatty acids, produced nonalcoholic fatty liver disease, and provoked endothelial dysfunction in female rats in association with Toll-like receptor 4 signaling-mediated vascular reactive oxygen species production. Limited consumption of a Western diet in premenopausal women may decrease their risk of cardiovascular complications.

A link between heart failure (HF) and low thyroid hormone (TH) function has been known for over a century. Nonetheless, there is a general belief that TH treatment of patients with HF may not be worth the risk. This is largely based on two clinical trials where heart patients were treated with excessive doses of TH analogs, not actual THs. Further complicating the matter is the fact that normalization of THs in noncardiac patients can often be challenging. This issue is not going away as noted by a steady increase in TH-HF citations in recent years. In this article, we discuss what we know and how we may move the field forward.

Background Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This “proof-of-concept” study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. Objective The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules. Methods We have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “Semantic Sensor Network Ontology” and “Systematized Nomenclature of Medicine—Clinical Terms” to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based “Jena Framework” (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the “HermiT 1.4.3.x” ontology reasoner available in Protégé 5.x. Results The proposed ontology has been implemented for the study case “obesity.” However, it can be extended further to other lifestyle diseases. “UiA eHealth Ontology” has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with “Owl Viz,” and the formal representation has been used to infer a participant’s health status using the “HermiT” reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks. Conclusions This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.

Background We systematically reviewed the literature on human coaching to identify different coaching processes as behavioral interventions and methods within those processes. We then reviewed how those identified coaching processes and the used methods can be utilized to improve an electronic coaching (eCoaching) process for the promotion of a healthy lifestyle with the support of information and communication technology (ICT). Objective This study aimed to identify coaching and eCoaching processes as behavioral interventions and the methods behind these processes. Here, we mainly looked at processes (and corresponding models that describe coaching as certain processes) and the methods that were used within the different processes. Several methods will be part of multiple processes. Certain processes (or the corresponding models) will be applicable for both human coaching and eCoaching. Methods We performed a systematic literature review to search the scientific databases EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MDPI, Google Scholar, and PubMed for publications that included personal coaching (from 2000 to 2019) and persuasive eCoaching as behavioral interventions for a healthy lifestyle (from 2014 to 2019). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework was used for the evidence-based systematic review and meta-analysis. Results The systematic search resulted in 79 publications, including 72 papers and seven books. Of these, 53 were related to behavioral interventions by eCoaching and the remaining 26 were related to human coaching. The most utilized persuasive eCoaching methods were personalization (n=19), interaction and cocreation (n=17), technology adoption for behavior change (n= 17), goal setting and evaluation (n=16), persuasion (n=15), automation (n=14), and lifestyle change (n=14). The most relevant methods for human coaching were behavior (n=23), methodology (n=10), psychology (n=9), and mentoring (n=6). Here, “n” signifies the total number of articles where the respective method was identified. In this study, we focused on different coaching methods to understand the psychology, behavioral science, coaching philosophy, and essential coaching processes for effective coaching. We have discussed how we can integrate the obtained knowledge into the eCoaching process for healthy lifestyle management using ICT. We identified that knowledge, coaching skills, observation, interaction, ethics, trust, efficacy study, coaching experience, pragmatism, intervention, goal setting, and evaluation of coaching processes are relevant for eCoaching. Conclusions This systematic literature review selected processes, associated methods, strengths, and limitations for behavioral interventions from established coaching models. The identified methods of coaching point toward integrating human psychology in eCoaching to develop effective intervention plans for healthy lifestyle management and overcome the existing limitations of human coaching.

BACKGROUND Digital interventions have tremendous potential to improve well-being and health care conveyance by improving adequacy, proficiency, availability, and personalization. They have gained acknowledgment in interventions for the management of a healthy lifestyle. Therefore, we are reviewing existing conceptual frameworks, digital intervention approaches, and associated methods to identify the impact of digital intervention on adopting a healthier lifestyle. OBJECTIVE This study aims to evaluate the impact of digital interventions on weight management in maintaining a healthy lifestyle (eg, regular physical activity, healthy habits, and proper dietary patterns). METHODS We conducted a systematic literature review to search the scientific databases (Nature, SpringerLink, Elsevier, IEEE Xplore, and PubMed) that included digital interventions on healthy lifestyle, focusing on preventing obesity and being overweight as a prime objective. Peer-reviewed articles published between 2015 and 2020 were included. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and a framework for an evidence-based systematic review. Furthermore, we improved the review process by adopting the Rayyan tool and the Scale for the Assessment of Narrative Review Articles. RESULTS Our initial searches identified 780 potential studies through electronic and manual searches; however, 107 articles in the final stage were cited following the specified inclusion and exclusion criteria. The identified methods for a successful digital intervention to promote a healthy lifestyle are self-monitoring, self-motivation, goal setting, personalized feedback, participant engagement, psychological empowerment, persuasion, digital literacy, efficacy, and credibility. In this study, we identified existing conceptual frameworks for digital interventions, different approaches to provide digital interventions, associated methods, and execution challenges and their impact on the promotion of healthy lifestyle management. CONCLUSIONS This systematic literature review selected intervention principles (rules), theories, design features, ways to determine efficient interventions, and weaknesses in healthy lifestyle management from established digital intervention approaches. The results help us understand how digital interventions influence lifestyle management and overcome the existing shortcomings. It serves as a basis for further research with a focus on designing, developing, testing, and evaluating the generation of personalized lifestyle recommendations as a part of digital health interventions.

Societal factors such as globalization, supermarket growth, rapid unplanned urbanization, sedentary lifestyle, economical distribution, and social position gradually develop behavioral risk factors in humans. Behavioral risk factors are unhealthy habits (consumption of tobacco and alcohol), improper diet (consumption of high calorific discretionary fast foods, sweet beverages), and physical inactivity. The behavioral risks may lead to physiological risks, body–energy imbalance. Obesity is one of the foremost lifestyle diseases that leads to other health conditions, such as cardiovascular disease (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the boundary of age and socio-economic background. “World health organization (WHO)” has predicted that lifestyle diseases will claim 71–73% of the global death, by the end of 2020. It can be prevented with proper identification of associated risk factors and appropriate behavioral intervention plans. The key determinants of obesity are—a. age, b. weight, c. height, and d. body mass index (BMI). This paper addresses the potential of ensemble machine learning approaches to assess the associated risk factors of obesity through the evaluation of existing, publicly accessible health datasets, such as “Kaggle”, and “UCI”. Followed by, we compared our identified risk factors with the obtained risk factors from literature study. In future, we are intending to reuse the obtained knowledge to collect data from a controlled trial of adult population (age between 20 and 60) in south Norway to generate personalized, contextual, and behavioral recommendations with a smart electronic coaching (eCoaching) system for behavioral intervention for the promotion of healthy lifestyle.

A human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

In this paper, we analyzed the holistic impact of external weather on chronic pulmonary infection in the Agder region with traditional machine learning algorithms. Millions of people are diagnosed with Chronic Obstructive Pulmonary Disease (COPD). Our study is dedicated in the Agder region, the Southern part of Norway. Norway has four seasons – winter (December-February), late winter/spring (March-May), Summer (June-August), and Autumn (September-November) in a year with average annual temperature approx. 7.5 °C | 45.5 °F and an annual rainfall of 1260 mm or 49.6 in. in Kristiansand. As predicted by the World Health Organization (WHO), in 2016, Norway suffered from 8% mortality due to c(1)hronic respiratory diseases. The disease is strongly afflicted by meteorological and environmental factors in distinct regions worldwide. This article explores correlation and dependency between COPD and temperature, barometric pressure, and humidity in the Agder region. We relate multiple prognostic models for patient evaluation based on patient condition and weather information. The COPD data were accumulated from 94 patients that reside in the Agder region as part of a major EU-funded project called “United for Health”. Outcomes showed that there is a dependence between air temperature and the patient’s condition. However, there is no significant dependency between air pressure and humidity, and the patient assessment results in that region. The results help to predict the condition of COPD patients in south Norway based on the weather forecast.

Health data are a sensitive category of personal data. They can result in a high risk to individuals and health information-handling rights and opportunities unless there is a sufficient defense. Reasonable security standards are needed to protect electronic health records (EHRs). All personal data handling needs adequate explanation. Maintaining access to medical data, even in the developing world, would favor health and well-being across the world. Unfortunately, there are still countries that hinder the portability of medical records. Numerous occurrences have shown that it still takes weeks for medical data to be ported from one general physician to another. Cross-border portability is almost impossible due to the lack of technical infrastructure and standardization. The authors demonstrate the difficulty of the portability of medical records with some example case studies as a collaborative engagement exercise through a data-mapping process to describe how different people and datapoints interact and evaluate EHR portability techniques. They then propose a blockchain-based EHR system that allows secure and cross-border sharing of medical data. The ethical and technical challenges around having such a system are also been discussed.

This study aims to implement the following four advanced pattern recognition algorithms, such as “optimal Bayesian classifier,” “anti-Bayesian classifier,” “decision trees (DTs),” and “dependence trees (DepTs)” on both artificial and real datasets for multi-class classification. Then, we calculated the performance of individual algorithms on both real and artificial data for comparison. In Sect. 1, a brief introduction is given about the study. In the second section, the different types of datasets used in this study are discussed. In the third section, we compared the classification accuracies of Bayesian and anti-Bayesian methods for both the artificial and real-life datasets. In the fourth section, a comparison between the classification accuracy of DT and DepT classification methods for both the artificial and real-life datasets is discussed. In the fifth section, a comparison between the classification accuracy of the four algorithms, such as (a) Bayes, (b) anti-Bayes, (c) DTs, and (d) DepTs for both the artificial and real datasets is explained. We used 5-fold cross-validation to determine the classification accuracy of individual, machine learning-based, advanced pattern recognition (PR) models.

Background Digital interventions have tremendous potential to improve well-being and health care conveyance by improving adequacy, proficiency, availability, and personalization. They have gained acknowledgment in interventions for the management of a healthy lifestyle. Therefore, we are reviewing existing conceptual frameworks, digital intervention approaches, and associated methods to identify the impact of digital intervention on adopting a healthier lifestyle. Objective This study aims to evaluate the impact of digital interventions on weight management in maintaining a healthy lifestyle (eg, regular physical activity, healthy habits, and proper dietary patterns). Methods We conducted a systematic literature review to search the scientific databases (Nature, SpringerLink, Elsevier, IEEE Xplore, and PubMed) that included digital interventions on healthy lifestyle, focusing on preventing obesity and being overweight as a prime objective. Peer-reviewed articles published between 2015 and 2020 were included. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and a framework for an evidence-based systematic review. Furthermore, we improved the review process by adopting the Rayyan tool and the Scale for the Assessment of Narrative Review Articles. Results Our initial searches identified 780 potential studies through electronic and manual searches; however, 107 articles in the final stage were cited following the specified inclusion and exclusion criteria. The identified methods for a successful digital intervention to promote a healthy lifestyle are self-monitoring, self-motivation, goal setting, personalized feedback, participant engagement, psychological empowerment, persuasion, digital literacy, efficacy, and credibility. In this study, we identified existing conceptual frameworks for digital interventions, different approaches to provide digital interventions, associated methods, and execution challenges and their impact on the promotion of healthy lifestyle management. Conclusions This systematic literature review selected intervention principles (rules), theories, design features, ways to determine efficient interventions, and weaknesses in healthy lifestyle management from established digital intervention approaches. The results help us understand how digital interventions influence lifestyle management and overcome the existing shortcomings. It serves as a basis for further research with a focus on designing, developing, testing, and evaluating the generation of personalized lifestyle recommendations as a part of digital health interventions.

— In a smart healthcare system," Human Activity Recognition (HAR)" is considered as an efficient approach in pervasive computing from activity sensor readings. The "Ambient Assisted Living (AAL)" in the home or community helps the people to provide independent care and enhanced living quality. However, many AAL models are restricted to multiple factors that include both the computational cost and system complexity. Moreover, the HAR concept has more relevance because of its applications, such as content-based video search, sports play analysis, crowd behavior prediction systems, patient monitoring systems, and surveillance systems. This paper attempts to implement the HAR system using a popular deep learning algorithm, namely "Recurrent Neural Network (RNN)" with the activity data collected from smart activity sensors over time, and it is publicly available in the "UC Irvine Machine Learning Repository (UCI)". The proposed model involves three processes: (1) data collection, (b) optimal feature learning, and (c) activity recognition. The data gathered from the benchmark repository was initially subjected to optimal feature selection that helped to select the most significant features. The proposed optimal feature selection method is based on a new meta-heuristic algorithm called "Colliding Bodies Optimization (CBO)". An objective function derived from the recognition accuracy has been used for accomplishing the optimal feature selection. The proposed model on the concerned benchmark dataset outperformed the conventional models with enhanced performance.

Healthcare data in cloud computing facilitates the treatment of patients efficiently by sharing information about personal health data between the healthcare providers for medical consultation. Furthermore, retaining the confidentiality of data and patients' identity is a another challenging task. This paper presents the concept of an access control-based (AC) privacy preservation model for the mutual authentication of users and data owners in the proposed digital system. The proposed model offers a high-security guarantee and high efficiency. The proposed digital system consists of four different entities, user, data owner, cloud server, and key generation center (KGC). This approach makes the system more robust and highly secure, which has been verified with multiple scenarios. Besides, the proposed model consisted of the setup phase, key generation phase, encryption phase, validation phase, access control phase, and data sharing phase. The setup phases are run by the data owner, which takes input as a security parameter and generates the system master key and security parameter. Then, in the key generation phase, the private key is generated by KGC and is stored in the cloud server. After that, the generated private key is encrypted. Then, the session key is generated by KGC and granted to the user and cloud server for storing, and then, the results are verified in the validation phase using validation messages. Finally, the data is shared with the user and decrypted at the user-end. The proposed model outperforms other methods with a maximal genuine data rate of 0.91.

BACKGROUND Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This “proof-of-concept” study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. OBJECTIVE The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules. METHODS We have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “Semantic Sensor Network Ontology” and “Systematized Nomenclature of Medicine—Clinical Terms” to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based “Jena Framework” (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the “HermiT 1.4.3.x” ontology reasoner available in Protégé 5.x. RESULTS The proposed ontology has been implemented for the study case “obesity.” However, it can be extended further to other lifestyle diseases. “UiA eHealth Ontology” has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with “Owl Viz,” and the formal representation has been used to infer a participant’s health status using the “HermiT” reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks. CONCLUSIONS This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.

BACKGROUND We systematically reviewed the literature on human coaching to identify different coaching processes as behavioral interventions and methods within those processes. We then reviewed how those identified coaching processes and the used methods can be utilized to improve an electronic coaching (eCoaching) process for the promotion of a healthy lifestyle with the support of information and communication technology (ICT). OBJECTIVE This study aimed to identify coaching and eCoaching processes as behavioral interventions and the methods behind these processes. Here, we mainly looked at processes (and corresponding models that describe coaching as certain processes) and the methods that were used within the different processes. Several methods will be part of multiple processes. Certain processes (or the corresponding models) will be applicable for both human coaching and eCoaching. METHODS We performed a systematic literature review to search the scientific databases EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MDPI, Google Scholar, and PubMed for publications that included personal coaching (from 2000 to 2019) and persuasive eCoaching as behavioral interventions for a healthy lifestyle (from 2014 to 2019). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework was used for the evidence-based systematic review and meta-analysis. RESULTS The systematic search resulted in 79 publications, including 72 papers and seven books. Of these, 53 were related to behavioral interventions by eCoaching and the remaining 26 were related to human coaching. The most utilized persuasive eCoaching methods were personalization (n=19), interaction and cocreation (n=17), technology adoption for behavior change (n= 17), goal setting and evaluation (n=16), persuasion (n=15), automation (n=14), and lifestyle change (n=14). The most relevant methods for human coaching were behavior (n=23), methodology (n=10), psychology (n=9), and mentoring (n=6). Here, “n” signifies the total number of articles where the respective method was identified. In this study, we focused on different coaching methods to understand the psychology, behavioral science, coaching philosophy, and essential coaching processes for effective coaching. We have discussed how we can integrate the obtained knowledge into the eCoaching process for healthy lifestyle management using ICT. We identified that knowledge, coaching skills, observation, interaction, ethics, trust, efficacy study, coaching experience, pragmatism, intervention, goal setting, and evaluation of coaching processes are relevant for eCoaching. CONCLUSIONS This systematic literature review selected processes, associated methods, strengths, and limitations for behavioral interventions from established coaching models. The identified methods of coaching point toward integrating human psychology in eCoaching to develop effective intervention plans for healthy lifestyle management and overcome the existing limitations of human coaching.