# Human Activity Recognition (HAR) in Healthcare
## 2 An Overview of Published Articles
Familiarity with exercise showed three adherence behaviors (high adherence, low adherence, and transient adherence), while exercise sessions after the familiarization phase resulted in adherent and non-adherent clusters. Two model types, namely repetitive decision trees (DT) and random forest (RF), were used to predict long-term adherence. The data to develop the DT model were patient clusters created based on baseline characteristics and clusters related to adherence to the exercise program. Since the DT model is unstable, a slight variation in the training dataset can lead to changes in the tree. A random forest (RF) technique, which is more stable, was thus applied. The first model showed both high accuracy and high recall, at 80.2 \(\pm\) 19.5% and 94.4 \(\pm\) 14.5%, respectively, which were better than the performance of the second model, which displayed a precision of 71.8 \(\pm\) 25.8% and a recall of 87.7 \(\pm\) 24%. Network analysis was applied to discover correlations of their characteristics that relate to adherence. This study highlighted how important the combination of basic clinical data with the characteristics acquired during a brief familiarization phase is for the high-accuracy prediction of adherence to the long-term RC program. The proposed methodology can be generalized to facilitate the identification of patients who are more adherent to telerehabilitation programs. Obesity increases the risk of many chronic diseases, especially cardiovascular disease, and is a cause of death. Faced with the rapid increase in obesity in the population, [PERSON] et al. (contribution 8) developed a cross-sectional analytical study of residents of the United States of America (USA) who have an Instagram account to determine whether using any meal tracking platform to record food consumption correlated with an improvement in body mass index (BMI). The survey was conducted on a sample of actual or graduate students from Mary Hardin Baylor University, Oakland University, the University of Kentucky, and Queens University in Charlotte. Eight hundred and ninety-six subjects with an Instagram account signed up to participate in an anonymous online survey, of which 78.7% were women, 20.6% were men, and 0.7% were classified as others. As for generations, 11.5% belonged to Generation Z, 75.6% to the Millenianis, 11.4% to Generation X, and 1.6% to the Baby Boomers. Overall, 93.5% of the sample did not smoke, 2.3% smoked, and 4.1% smoked occasionally. Concerning academic qualifications, 3.7% had high school graduates, 6.1% had some university credits, 0.6% had technical training, 3.2% had an associate degree, 43.2% had a bachelor's degree, 15.1% had a master's degree and 28.1% had a doctorate. The information acquired through the questionnaire included the number of hours per week dedicated to Instagram or physical activity and the intensity of physical activity performed. In order to test the influence of using any meal tracking platform to record food intake on BMI, they were asked if they had used any digital platform in the past month. The chi-square test was used to study the relationships between the use of any digital platform in the last month and gender, generation, smoking habits, highest academic degree earned, and time spent on Instagram. The Mann-Whitney U test was adopted to compare BMI, weekly hours spent on Instagram looking at nutrition- or physical activity-related content, vigorous physical activity, moderate physical activity, time spent walking, and time spent sitting among participants who did not eat meals. The survey showed that the platform was used by 34.2% of the sample. Participants who used any meal tracking platform also had a higher BMI, invested more hours per week on Instagram looking at nutrition- or physical activity-related content, and performed more minutes per week of vigorous physical activity. The survey showed that participants rely on new technologies for optimal weight without obtaining practical results.