Ingénierie des connaissances pour le choix d'interventions numériques de santé application en prévention du risque cardiovasculaire

Connected health services are seen as tools with great potential for changing people's lifestyles. Theyinduce positive changes towards the adoption of behaviors that are more beneficial to health. In the context ofcardiovascular disease risk prevention, the research hypothesis is that the user profile (medical, behavioral andsocial) predicts the type of connected application that will be an effective lever for the prevention of thesediseases. The main objective of the thesis is to design, develop and evaluate a decision support system to help inthe selection of the lever that best guarantees the follow-up of recommendations in primary prevention ofcardiovascular risk. Initially, a systematic review of the literature was carried out to identify the different typesof interventions, focusing in particular on the risk factors they target and on the question of users' adherence tothese interventions over time. The design of the decision support system was based on the one hand on a datamodel, inspired by a real-life data set from the GAZEL cohort and, on the other hand, on institutional knowledge,in the form of recommendations formalised by decision rules. The decision support system was implemented ina mobile application. A qualitative and ergonomic evaluation of the mobile application using the end-userversion of the u MARS (User Version of the Mobile Application Rating Scale) was carried out on 52 users agedbetween 18 and 60. The first version of the application received an overall rating of 4/5 on uMars. Oneperspective of this work is to reinforce the personalization of the choice of levers to optimize the adherence andimpact of these technologies. This will require conducting evaluations on a larger scale and over longer periodsof timeLes services de santé connectés sont considérés comme des outils à fort potentiel pour la modification du mode de vie des personnes. Ils induisent en effet des changements positifs vers l’adoption de comportements plus bénéfiques pour la santé. En nous situant dans le contexte de la prévention du risque de maladies cardiovasculaires ,l'hypothèse de recherche est que le profil de l'utilisateur (médical, comportemental et social) permet de prédire le type d'application connectée qui constituera pour cet utilisateur un levier efficace pour la prévention de ces maladies. L'objectif principal de la thèse est de concevoir, développer et évaluer un système d’aide à la décision pour l’aide à la sélection du levier garantissant au mieux le suivi des recommandations en prévention primaire du risque cardio-vasculaire. Dans un premier temps, une revue systématique de la littérature a été réalisée pour identifier les différents types d'interventions en s'intéressant en particulier aux facteurs de risques qu'elles ciblent et à la question de l'adhésion dans le temps des utilisateurs à ces interventions. La conception du système d'aide à la décision s'est appuyée d'une part sur un modèle de données, inspiré d’un jeu de données envie réelle issue de la cohorte GAZEL et, d'autre part, sur des connaissances institutionnelles, sous forme de recommandations formalisées par des règles de décision. Le système d'aide à la décision a été implémenté dans une application mobile. Une évaluation qualitative et ergonomique de l’application mobile à l’aide de la version des utilisateurs finaux de l’échelle de notation u MARS (User Version of the Mobile Application Rating Scale) a été effectuée sur 52 utilisateurs de 18 à 60 ans. La première version de l’application a obtenu une note globale de 4/5 sur uMars. Une perspective de ce travail consiste à renforcer la personnalisation du choix des leviers pour optimiser l'adhésion et l'impact de ces technologies. Cela nécessitera de conduire des évaluations à plus grande échelle et sur des temps plus long

Knowledge engineering for the choice of digital health interventions application in cardiovascular risk prevention

Les services de santé connectés sont considérés comme des outils à fort potentiel pour la modification du mode de vie des personnes. Ils induisent en effet des changements positifs vers l’adoption de comportements plus bénéfiques pour la santé. En nous situant dans le contexte de la prévention du risque de maladies cardiovasculaires ,l'hypothèse de recherche est que le profil de l'utilisateur (médical, comportemental et social) permet de prédire le type d'application connectée qui constituera pour cet utilisateur un levier efficace pour la prévention de ces maladies. L'objectif principal de la thèse est de concevoir, développer et évaluer un système d’aide à la décision pour l’aide à la sélection du levier garantissant au mieux le suivi des recommandations en prévention primaire du risque cardio-vasculaire. Dans un premier temps, une revue systématique de la littérature a été réalisée pour identifier les différents types d'interventions en s'intéressant en particulier aux facteurs de risques qu'elles ciblent et à la question de l'adhésion dans le temps des utilisateurs à ces interventions. La conception du système d'aide à la décision s'est appuyée d'une part sur un modèle de données, inspiré d’un jeu de données envie réelle issue de la cohorte GAZEL et, d'autre part, sur des connaissances institutionnelles, sous forme de recommandations formalisées par des règles de décision. Le système d'aide à la décision a été implémenté dans une application mobile. Une évaluation qualitative et ergonomique de l’application mobile à l’aide de la version des utilisateurs finaux de l’échelle de notation u MARS (User Version of the Mobile Application Rating Scale) a été effectuée sur 52 utilisateurs de 18 à 60 ans. La première version de l’application a obtenu une note globale de 4/5 sur uMars. Une perspective de ce travail consiste à renforcer la personnalisation du choix des leviers pour optimiser l'adhésion et l'impact de ces technologies. Cela nécessitera de conduire des évaluations à plus grande échelle et sur des temps plus longsConnected health services are seen as tools with great potential for changing people's lifestyles. Theyinduce positive changes towards the adoption of behaviors that are more beneficial to health. In the context ofcardiovascular disease risk prevention, the research hypothesis is that the user profile (medical, behavioral andsocial) predicts the type of connected application that will be an effective lever for the prevention of thesediseases. The main objective of the thesis is to design, develop and evaluate a decision support system to help inthe selection of the lever that best guarantees the follow-up of recommendations in primary prevention ofcardiovascular risk. Initially, a systematic review of the literature was carried out to identify the different typesof interventions, focusing in particular on the risk factors they target and on the question of users' adherence tothese interventions over time. The design of the decision support system was based on the one hand on a datamodel, inspired by a real-life data set from the GAZEL cohort and, on the other hand, on institutional knowledge,in the form of recommendations formalised by decision rules. The decision support system was implemented ina mobile application. A qualitative and ergonomic evaluation of the mobile application using the end-userversion of the u MARS (User Version of the Mobile Application Rating Scale) was carried out on 52 users agedbetween 18 and 60. The first version of the application received an overall rating of 4/5 on uMars. Oneperspective of this work is to reinforce the personalization of the choice of levers to optimize the adherence andimpact of these technologies. This will require conducting evaluations on a larger scale and over longer periodsof tim

Influence of Connected Health Interventions for Adherence to Cardiovascular Disease Prevention: A Scoping Review

International audienceAbstract Background Recent health care developments include connected health interventions to improve chronic disease management and/or promote actions reducing aggravating risk factors for conditions such as cardiovascular diseases. Adherence is one of the main challenges for ensuring the correct use of connected health interventions over time. Objective This scoping review deals with the connected health interventions used in interventional studies, describing the ways in which these interventions and their functions effectively help patients to deal with cardiovascular risk factors over time, in their own environments. The objective is to acquire knowledge and highlight current trends in this field, which is currently both productive and immature. Methods A structured literature review was constructed from Medline-indexed journals in PubMed. We established inclusion criteria relating to three dimensions (cardiovascular risk factors, connected health interventions, and level of adherence). Our initial search yielded 98 articles; 78 were retained after screening on the basis of title and abstract, 49 articles underwent full-text screening, and 24 were finally retained for the analysis, according to preestablished inclusion criteria. We excluded studies of invasive interventions and studies not dealing with digital health. We extracted a description of the connected health interventions from data for the population or end users. Results We performed a synthetic analysis of outcomes, based on the distribution of bibliometrics, and identified several connected health interventions and main characteristics affecting adherence. Our analysis focused on three types of user action: to read, to do, and to connect. Finally, we extracted current trends in characteristics: connect, adherence, and influence. Conclusion Connected health interventions for prevention are unlikely to affect outcomes significantly unless other characteristics and user preferences are considered. Future studies should aim to determine which connected health design combinations are the most effective for supporting long-term changes in behavior and for preventing cardiovascular disease risks

Encouraging Behavior Changes and Preventing Cardiovascular Diseases Using the Prevent Connect Mobile Health App: Conception and Evaluation of App Quality

International audienceBackground: Cardiovascular diseases are a major cause of death worldwide. Mobile health apps could help in preventing cardiovascular diseases by improving modifiable risk factors such as eating habits, physical activity levels, and alcohol or tobacco consumption.Objective: The aim of this study was to design a mobile health app, Prevent Connect, and to assess its quality for (1) assessing patient behavior for 4 cardiovascular risk factors (unhealthy eating, sedentary lifestyle, alcohol, and tobacco consumption) and (2) suggesting personalized recommendations and mobile health interventions for risky behaviors.Methods: The knowledge base of the app is based on French national recommendations for healthy eating, physical activity, and limiting alcohol and tobacco consumption. It contains a list of patient behaviors and related personalized recommendations and digital health interventions. The interface was designed according to usability principles. Its quality was assessed by a panel of 52 users in a 5-step process: completion of the demographic form, visualization of a short presentation of the app, testing of the app, completion of the user version of the Mobile App Rating Scale (uMARS), and an open group discussion.Results: This app assesses patient behaviors through specific questionnaires about 4 risk factors (unhealthy eating, sedentary lifestyle, alcohol, and tobacco consumption) and suggests personalized recommendations and digital health interventions for improving behavior. The app was deemed to be of good quality, with a mean uMARS quality score of 4 on a 5-point Likert scale. The functionality and information content of the app were particularly appreciated, with a mean uMARS score above 4. Almost all the study participants appreciated the navigation system and found the app easy to use. More than three-quarters of the study participants found the app content relevant, concise, and comprehensive. The aesthetics and the engagement of the app were also appreciated (uMARS score, 3.7). Overall, 80% (42/52) of the study participants declared that the app helped them to become aware of the importance of addressing health behavior, and 65% (34/52) said that the app helped motivate them to change lifestyle habits.Conclusions: The app assessed the risky behaviors of the patients and delivered personalized recommendations and digital health interventions for multiple risk factors. The quality of the app was considered to be good, but the impact of the app on behavior changes is yet to be demonstrated and will be assessed in further studies

Decision Support System for Selection of e-Health Interventions.

International audienceThe main goal of this work was to design a decision support system for effective personalized cardiovascular risk prevention: i) to identify behavioral groups associated with clinical risk factors, ii) to provide recommendations associated with the objective to be achieved and iii) to determine the decision-making rules assigning each group to the type of mobile health intervention conveying the most appropriate prevention messages, to help patients to achieve attainable goals. The system is based on an existing data prediction model taking into account specific risky behaviors, clinical risk factors and social status, and it is embedded in a new e-health application. The system is operational. The next step will be the design of a large study to assess improvements in patient adherence to prevention messages through e-health interventions selected by the application

De novo mutations in HCN1 cause early infantile epileptic encephalopathy

Hyperpolarization-activated, cyclic nucleotide gated (HCN) channels contribute to cationic current in neurons and regulate the excitability of neuronal networks. Studies in rat models have shown that the Hcn1 gene has a key role in epilepsy, but clinical evidence implicating HCN1 mutations in human epilepsy is lacking. We carried out exome sequencing for parent-offspring trios with fever-sensitive, intractable epileptic encephalopathy, leading to the discovery of two de novo missense HCN1 mutations. Screening of follow-up cohorts comprising 157 cases in total identified 4 additional amino acid substitutions. Patch-clamp recordings of I-h, currents in cells expressing wild-type or mutant human HCN1 channels showed that the mutations had striking but divergent effects on homomeric channels. Individuals with mutations had clinical features resembling those of Dravet syndrome with progression toward atypical absences, intellectual disability and autistic traits. These findings provide clear evidence that de novo HCN1 point mutations cause a recognizable earlyonset epileptic encephalopathy in humans