College is a critical time where students are more prone to engage in risky health behaviors known to negatively affect well-being, such as physical inactivity, stress, and poor dietary habits. A health promoting lifestyle is an important determinant of health status and is recognized as a major factor for the maintenance and improvement of health. This study was designed to assess the health-promoting lifestyle of students in health colleges and non-health colleges in Saudi Arabia. A total of 1656 students participated in this descriptive cross-sectional study. Data gathering was conducted from November 2016 to February 2017 at King Saud University. Participating students completed a self-reported questionnaire that included questions regarding their demographic characteristics and their health-promoting behaviors. The majority of participants were females (70.4%), 20% of the participants were overweight and 11.3%, were obese. The analysis showed that there was a significant difference between health colleges and non-health colleges with regards to the factor of health responsibility. Students at both schools were found to have an inadequate level of adherence to recommendations regarding physical activity and healthy eating habits. The analysis also found that majority of the students in both colleges do not attend educational programs on health care. The model shows that gender, type of college, year in school, and family structure were significant predictors of the health lifestyle of students in Saudi Arabia. The results of the current study indicate that university students are leading unhealthy lives, where the majority of them have unhealthy eating habits and poor physical activity level. Universities are ideal settings for implementing health promotion programs. Therefore, planning and implementing programs to motivate students to be more responsible for their own health, to engage more in physical activity, and to practice healthy eating habits and other forms of wellness are of paramount importance.

In this cross-sectional study, we aimed to determine the association of self-care management practices and glycemic control of type 2 diabetes mellitus in Saudi Arabia. A total of 352 type 2 diabetes mellitus (T2DM) patients from two public tertiary hospitals in Saudi Arabia participated in this study. All T2DM patients were recruited and interviewed by a researcher between January to April 2018 from the outpatient diabetes clinics. All respondents answered a four-part questionnaire which includes demographics data, Diabetes Self-Management Questionnaire (DSMQ). Linear Regression was performed to assess the significance of predictors and compute the coefficient of determination. The mean age of the participants was 51.89 ± 10.94. Of the 352 participants, 52% were obese (BMI: ≥30 kgm2) and 77% of the participants had glycated haemoglobin (HbA1c) over 7%. The analysis showed that subscale of Glucose management was the strongest predictor of Hba1c levels of participants’ followed by physical activity. Gender and marital status emerged as significant predictors for their self-care management practices. Female patients had more self-care management practices than male patients (B 0.20; 95CI 0.10– 0.96 (p = 0.015). This study provides an evidence on the self-care management of T2DM patients in Saudi Arabia. The high self-care management found in the study highlights that the patients are aware of the severity of and possible complications associated with T2DM.

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.