Cross-Sectional Analysis of University Students' Health Using a Digitised Health Survey.
Pier A SpinazzeMarise J KasteleynJiska J AardoomJosip CarNiels H ChavannesPublished in: International journal of environmental research and public health (2020)
University student years are a particularly influential period, during which time students may adopt negative behaviours that set the precedent for health outcomes in later years. This study utilised a newly digitised health survey implemented during health screening at a university in Singapore to capture student health data. The aim of this study was to analyze the health status of this Asian university student population. A total of 535 students were included in the cohort, and a cross-sectional analysis of student health was completed. Areas of concern were highlighted in student's body weight, visual acuity, and binge drinking. A large proportion of students were underweight (body mass index (BMI) < 18.5)-18.9% of females and 10.6% of males-and 7% of males were obese (BMI > 30). Although the overall prevalence of alcohol use was low in this study population, 9% of females and 8% of males who consumed alcohol had hazardous drinking habits. Around 16% of these students (male and female combined) typically drank 3-4 alcoholic drinks each occasion. The prevalence of mental health conditions reported was very low (<1%). This study evaluated the results from a digitised health survey implemented into student health screening to capture a comprehensive health history. The results reveal potential student health concerns and offer the opportunity to provide more targeted student health campaigns to address these.
Keyphrases
- mental health
- public health
- healthcare
- high school
- body mass index
- health information
- cross sectional
- health promotion
- medical students
- type diabetes
- human health
- body weight
- risk assessment
- adipose tissue
- physical activity
- big data
- weight gain
- medical education
- mental illness
- alcohol consumption
- drug delivery
- artificial intelligence
- deep learning