Biomedical

Persons’ experiences of having hypertension: An interview study





Abstract

Background among the 1–1.5 billion persons with hypertension globally only, 20–30% have controlled blood pressure (BP). The most important problem identified is non-adherence to treatment, i.e., failure to change lifestyle and to take prescribed medication. Knowledge about the reasons for this is limited. Objectives The aim of the study was to explore people's experiences of having hypertension. Design Inductive design based on qualitative interviews. Settings The south of Sweden. Participants Twelve adults diagnosed with hypertension and treated in primary care were interviewed. Methods The transcribed interviews were analysed using content analysis, which rendered three categories. Results The individuals adapted to their diagnosis in different ways. Collaboration with the staff gave security, but the persons still perceived anxiety and uncertainty. Conclusions To meet the needs of people with hypertension, strategies such as person-centred counselling and care, using digital interventions, following national guidelines and starting nurse-led clinics, may be of help. These strategies can give a foundation for increased self-efficacy, which is crucial for persons to be able to change lifestyle and adhere to prescribed medication in order to achieve BP control.

Key Question

What is the primary focus of this study?

The study investigates advanced numerical methods and algorithms for solving nonlinear stochastic models in applied sciences.

Why are nonlinear stochastic models important?

Nonlinear stochastic models are critical for accurately representing real-world systems influenced by randomness and complex interactions, such as weather patterns and financial markets.

What key methods are discussed in the paper?

The paper emphasizes techniques such as Monte Carlo simulations, stochastic Galerkin methods, and finite element approaches tailored for nonlinear stochastic systems.

How do these methods improve problem-solving in applied sciences?

These methods enhance computational efficiency and accuracy, enabling researchers to solve complex stochastic equations in areas like engineering, physics, and biology.

What are the practical applications of these findings?

The findings can be applied to optimize systems in fields such as climate modeling, risk analysis, structural engineering, and resource management.

Why is this research significant?

The research addresses critical computational challenges in solving nonlinear stochastic problems, paving the way for advancements in predictive modeling and decision-making.