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

BACKGROUND: 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.

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

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.

Decision Support System for Selection of e-Health Interventions.

The 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 Ih 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 Ih 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 early-onset epileptic encephalopathy in humans.

Seizure semiology in autosomal dominant epilepsy with auditory features, due to novel LGI1 mutations.

Mutations in LGI1 are found in 50% of families with autosomal dominant epilepsy with auditory features (ADEAF). In ADEAF, family members have predominantly lateral temporal lobe seizures but mesial temporal lobe semiology may also occur. We report here three families with novel LGI1 mutations (p.Ile82Thr, p.Glu225*, c.432-2_436del). Seven affected individuals reported an auditory aura and one a visual aura. A 10-year old boy described a cephalic aura followed by an unpleasant taste and oral automatisms without auditory, visual or psychic features.