Changes in Hypertension Diagnostic Criteria Enhance Early Identification of at Risk College Students

Health Screening Among a Rural, Hourly Workforce: North Carolina SERVIRE Project

INTRODUCTION

This study demonstrates the feasibility of a novel, business-partnered, and worksite-based approach to healthcare access to facilitate chronic disease screening and diagnosis among rural hourly workers. The prevalence of undiagnosed and untreated diabetes and hypertension among screening participants was determined.

METHODS

From February 2021 to June 2023, investigators partnered with 29 businesses to screen 1,114 workers. Health screenings included a demographic questionnaire, A1c testing for prediabetes (A1c of 5.7-6.4) and diabetes (A1c≥6.5), hypertension (Stage 1: systolic blood pressure of 130-139 mmHg; Stage 2: systolic blood pressure ≥140 mmHg), kidney disease (estimated glomerular filtration rate <60; urine protein ≥1+), and questionnaire assessment of stroke (CHA2DS2-VASc) and sleep apnea (STOP-bang) risk.

RESULTS

Of the 1,114 individuals screened (n=632, 56.7% male; n=497, 44.6% Black)), 388 (36%) screened positive for prediabetes or diabetes. Diabetes was previously undiagnosed in 273 (70.4%) of these participants. More than half of the participants (n=680, 62.4%) had an elevated blood pressure reading during the screening, and the majority of these participants (n=445, 65.4%) had not been previously diagnosed with hypertension. In addition, 241 (21.6%) participants were at an increased risk of stroke (CHA2DS2-VASc≥2), and 182 (23.7%) had a STOP-Bang score ≥4, indicating an increased risk of obstructive sleep apnea.

CONCLUSIONS

By partnering with local businesses to deliver worksite-based health screenings, high rates of undiagnosed and uncontrolled diabetes and hypertension were identified among the rural, hourly workforce. This worksite-based approach to healthcare access could facilitate early detection of chronic disease, improve patient engagement in the healthcare system, and ultimately yield better long-term public health outcomes.

Effect of Physical Exercise Intervention Based on Improved Neural Network on College Students’ Mental Health

Physical exercise refers to various physical exercises carried out through certain means and methods. Physical exercise can not only achieve the purpose of strengthening the body and health but also make people face challenges in physical and mental sports, and the resulting psychological satisfaction and excitement make exercisers happy physically and mentally. Physical activity has a certain appeal that makes it one of the most effective ways for modern people to alleviate mental illnesses. The human brain’s reaction time is linked to its thinking speed and intelligence. Regular physical activity can increase human brain cell reaction time and completely utilize human brain capacity, which is beneficial to the healthy development of human attention, memory, imagination, and thinking ability. Scientific and reasonable physical exercise is also an important means to improve people’s intelligence levels. Everyone’s physical health level is closely related to their mental health. A healthy mind comes from a healthy body. A large number of scientific studies have confirmed that reasonably arranging the contents and methods of physical exercise according to their own health level and psychological state cannot only enhance the physique of college students but also cultivate their excellent personality. Firstly, this paper summarizes the influence of physical exercise on mental health. The ways of employing physical exercise to improve and improve the mental health of college students are presented in this study. Then, this paper proposes a physical exercise intervention based on an improved neural network (NN), which has an impact on the mental health level of college students, and the effectiveness of this model is verified by simulation experiments.

Application of artificial intelligence in university sports risk recognition and identification

This article is based on artificial intelligence technology to recognize and identify risks in college sport. The application of motion recognition technology first need to collect the source data, store the collected data in the server database, collect the learner’s real-time data and return it to the database to achieve the purpose of real-time monitoring. It is found that in the identification of risk sources of sports courses, there are a total of 4 first-level risk factors, namely teacher factors, student factors, environmental factors, and school management factors, and a total of 15 second-level risk factors, which are teaching preparation, teaching process, and teaching effect. When the frequency of teaching risks is low, the consequence loss is small. When the frequency of teaching risks is low, the consequences are very serious. Risk mitigation is the main measure to reduce the occurrence of teaching risks and reduce the consequences of losses.