Personalization of Mobile Apps for Health Behavior Change: Protocol for a Cross-sectional Study.

BACKGROUND: Mobile health apps have the potential to motivate people to adopt healthier behavior, but many fail to maintain this behavior over time. However, it has been suggested that long-term adherence can be improved by personalizing the proposed interventions. Based on the literature, we created a conceptual framework for selecting appropriate functionalities according to the user's profile. OBJECTIVE: This cross-sectional study aims to investigate if the relationships linking functionalities and profiles proposed in our conceptual framework are confirmed by user preferences. METHODS: A web-based questionnaire comprising several sections was developed to determine the mobile app functionalities most likely to promote healthier behavior. First, participants completed questionnaires to define the user profile (Big Five Inventory-10, Hexad Scale, and perception of the social norm using dimensions of the Theory of Planned Behavior). Second, participants were asked to select the 5 functionalities they considered to be the most relevant to motivate healthier behavior and to evaluate them on a score ranging from 0 to 100. We will perform logistic regressions with the selected functionalities as dependent variables and with the 3 profile scales as predictors to allow us to understand the effect of the participants' scores on each of the 3 profile scales on the 5 selected functionalities. In addition, we will perform logistic ordinal regressions with the motivation score of the functionalities chosen as dependent variables and with scores of the 3 profile scales as predictors to determine whether the scores on the different profile scales predict the functionality score. RESULTS: Data collection was conducted between July and December 2021. Analysis of responses began in January 2022, with the publication of results expected by the end of 2022. CONCLUSIONS: This study will allow us to validate our conceptual model by defining the preferred functionalities according to user profiles. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/38603.

An Ontology-Based Semantic Model for Sharing and Reusability of Clinical Pathways Across Context (ShaRE-CP).

Clinical pathways (CP) enable a standardized and an efficient management of patients with common pathologies. As operational tools, they take into account knowledge from guidelines and from the context (e.g. availability of resources) in which different interventions are to be carried out. Mastering the coherence of interactions between all these knowledge domains is a major challenge for the implementation of CP. This scientific work led to the development of an ontology called Shareable and Reusable Clinical Pathway Ontology (ShaRE-CP) which integrates four knowledge domains (CP, guidelines, health resources and context) and to the establishment of existing semantic links between them. The consistency of this semantic model has been validated by using reasoners. This ontology can serve as a basis for the development of a decision support system for planning and managing patient care.

Personalization Dimensions for MHealth to Improve Behavior Change: A Scoping Review.

Due to the large number of smartphone users, mHealth has become a popular support to foster users' health behavior change Personalization is an important factor to increase the effectiveness of mHealth interventions. Based on a literature review, we have listed and categorized personalization concepts associated with behavior change in mHealth into 4 dimensions, users, system functionalities, information, and app properties. The users dimension refers to user-related characteristics such as personality, player profile, need for cognition and perception of social norms. The system functionalities contain the functionalities that can be found in applications such as reminders as well as gamification functionalities such as collectibles. The information dimension concerns the way information is transmitted, such as the source of the message must be expert or the type of feedback to be provided. Finally, there are app properties such as the aesthetics of the application. For the next part, it would be interesting to discover the links we can make between the dimensions.

[A synthesis of patient problems: a first step to the nursing minimum data set]. / Synthèse des problèmes de soins infirmiers: un pas vers la statistique infirmière.

UNLABELLED: In 2012 Switzerland will adopt a system for hospital funding, named SwissDRG, based on the German Diagnosis Related Group model. This method of funding takes little account of nursing as the grouping is based on diagnoses and treatments. OBJECTIVE: This study conducted at the University Hospital of Lausanne seeks to introduce nursing data in the medical statistic under the form of diagnosis. METHOD: Based on a web questionnaire, this study interviewed 293 nursing clinical and administrative managers. According to their experience they had to selected 10 problems of care from a list of 30 and sort them depending to their impact on the workload and length of stay. RESULTS: 95 responses from most of the clinical specialties of the University Hospital of Lausanne. A list of 15 nursing problems has been published. The use of the RIDITs shows a difference between the subgroups determined by sex and length of service. CONCLUSION: This study showed that the use of web questionnaires is a way that should be continued. It also shows that through the RIDITs it is possible to analyze the differences in responses between groups even if the sample is small. This study must now be extended to the pediatrics and obstetrics.