Brazilian health professionals' perceptions and knowledge about automated blood pressure monitors.
Nila Larisse Silva de AlbuquerqueRaj S PadwalThelma Leite de AraújoPublished in: Journal of human hypertension (2021)
Obtaining accurate blood pressure readings is vital. However, students and health professionals do not always receive adequate training on blood pressure measurement, especially regarding new technologies, leading to insufficient knowledge. Therefore, the aim of this study is to analyze Brazilian health professionals' perceptions and knowledge about automated blood pressure monitors. This cross-sectional study involved 1734 Brazilian nurses, nursing technicians, and doctors who reported having some experience of using automated monitors. Perceptions about differences between readings obtained through the auscultatory and oscillometric methods, influence of small differences in clinical decision-making, confidence in automated monitors, and knowledge about contraindications for the use of these devices were assessed. Most medical and nursing professionals considered differences of up to 5 mmHg (40.94%) between auscultatory and oscillometric measurements acceptable. Of these, 69.02% reported that even small differences can influence clinical decisions. Confidence in readings obtained using automated blood pressure monitors was reported by 53.92%. Among the motivations for making these devices available in health services, the most frequent was the saving of time (48.85%) and the least frequent, the perception that the use of this technology requires less training (9.40%). Arrhythmia was the most recognized contraindication for the use of automated monitors (28.49%), followed by obesity (28.14%) and blood pressure readings above 160 × 100 mmHg. In conclusion, there is a lack of knowledge about the functionalities and indications of blood pressure monitors and a low tolerance for measurements different from those obtained through manual mercury sphygmomanometers or aneroids.
Keyphrases
- blood pressure
- healthcare
- hypertensive patients
- heart rate
- machine learning
- deep learning
- high throughput
- primary care
- mental health
- decision making
- type diabetes
- blood glucose
- metabolic syndrome
- high resolution
- skeletal muscle
- weight loss
- adipose tissue
- weight gain
- single cell
- virtual reality
- high school
- glycemic control