Blood Pressure Trends, Demographic Data, Workload, and Lifestyle Factors Among Nurses in the Subcarpathian Region of Poland: A Cross-Sectional Observational Study.
Anna BartosiewiczEdyta ŁuszczkiMarta PieczonkaJustyna NowakLukasz OleksyArtur StolarczykAnna LewandowskaAgnieszka DymekPublished in: Medical science monitor : international medical journal of experimental and clinical research (2024)
BACKGROUND Hypertension is one of the main modifiable risk factors linked to cardiovascular disease and its prevalence is currently increasing in various age groups. This study aimed to evaluate blood pressure, demographic data, workload, and lifestyle factors in nurses employed in hospitals in the Subcarpathian region of southeastern Poland. MATERIAL AND METHODS This cross-sectional observational study was conducted among 627 professionally active nurses. Certified devices were used for measurements: body mass analyzer (Tanita MC-980 PLUS MA), automated sphygmomanometer (Welch Allyn 4200B), stadiometer (Seca 213), and tape measure (Seca 201). The frequency of consumption of specific product groups was assessed using a survey method. Analysis using R software (version 4.3.1) employed logistic regression to examine variables affecting hypertension occurrence. RESULTS The study found that elevated blood pressure is more prevalent among nurses than they self-report. Logistic regression analysis identified significant predictors for hypertension, including age (odds ratio; OR=1.061; OR=1.045), working more than 1 job (OR=1.579; OR=1.864), and body mass index (OR=1.152; OR=1.113). CONCLUSIONS Regular monitoring of blood pressure is necessary for early detection and timely intervention of hypertension. Enhancing nurses' awareness of their own health will encourage proactive preventive measures. Implementing comprehensive education programs focused on the latest advances in cardiovascular disease prevention is essential.
Keyphrases
- blood pressure
- healthcare
- cardiovascular disease
- mental health
- hypertensive patients
- risk factors
- heart rate
- body mass index
- cross sectional
- randomized controlled trial
- electronic health record
- blood glucose
- weight loss
- risk assessment
- machine learning
- quality improvement
- coronary artery disease
- deep learning
- big data
- cardiovascular risk factors
- depressive symptoms
- high throughput
- insulin resistance