Objective: to analyze, in the literature, evidence about the benefits of the integrative and complementary practice of Qigong with regard to the health of adults and the elderly. Method: a systematic review by searching for studies in the PubMed, CINAHL, LILACS, EMBASE and Cochrane Library databases. Randomized and non-randomized clinical trials were included; in Portuguese, English and Spanish; from 2008 to 2018. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses strategy was adopted, as well as the recommendation of the Cochrane Collaboration for assessing the risk of bias in the clinical trials analyzed. Results: 28 studies were selected that indicated the benefit of the practice to the target audience, which can be used for numerous health conditions, such as: cancer; fibromyalgia; Parkinson’s disease; Chronic Obstructive Pulmonary Disease; Burnout; stress; social isolation; chronic low back pain; cervical pain; buzz; osteoarthritis; fatigue; depression; and cardiovascular diseases. However, there was a great risk of bias in terms of the blinding of the research studies. Conclusion: the practice of Qigong produces positive results on health, mainly in the medium and long term. This study contributes to the advancement in the use of integrative and complementary practices in nursing, since it brings together the scientific production in the area from the best research results available.

This work proposes STORM, a solution for decision-making in a residential environment that combines fog computing and computational intelligence. In this scenario, STORm is able to collect, treat, disseminate, detect and control information generated from the sensor nodes to the decision- making process. With this in mind, STORm is based on the development of an ensemble of classifiers to enhance precision in the decision-making process, as well as on the use of the fog computing paradigm to manage and process the actions in the residence in real-time. The idea is to provide computational resources closer to the end-users, processes them locally before transmits them to the cloud. When compared with the classical approaches adopted in the literature for classification, the results show that, as well as providing a high degree of accuracy in the classification, the STORm maintains a high stability in the decision-making process.

Objective: to assess nursing students’ emotions undergoing maternal-child clinical simulation. Methods: an observational study, carried out between June and July 2019. The Focus Group technique was used, with 28 nursing students, randomly distributed into three groups, with qualitative (Bardin technique) and quantitative data (Artificial Intelligence) analysis, to analyze emotions through facial expressions, tone of voice and description of speeches. Results: we defined two categories: “It was not easy, it was very stressful”; and “Very valuable experience”. In Artificial Intelligence, emotional distribution between face, voice and speech revealed a prevalence of negative valence, medium-high degree of passivity, medium power to control the situation and medium-high degree of obstruction in task accomplishment. Final considerations: this study revealed an oscillation between positive and negative emotions, and shows to the importance of recognizing them in the teaching-learning process in mother-child simulation.