Towards understanding exercise adherence in chronic obstructive pulmonary disease
| PhD ceremony: | Ms E. (Ellen) Ricke |
| When: | September 28, 2023 |
| Start: | 12:45 |
| Supervisor: | prof. dr. A. (Arie) Dijkstra |
| Co-supervisor: | dr. E.W. Bakker |
| Where: | Academy building RUG |
| Faculty: | Behavioural and Social Sciences |
Attention to self-management in patients with chronic diseases is increasingly important for effective and efficient care. One crucial aspect of self-management is long-term adherence, which refers to the ability to maintain specific behavioral changes over time to control a disease. This research aimed to improve understanding of exercise adherence in people with chronic obstructive pulmonary disease (COPD).
The researchers developed a measurement instrument to assess exercise adherence in primary physiotherapy. They also conducted a comprehensive review of the literature to identify factors that may predict exercise adherence. Based on these predictors and the measurement instrument, the researchers developed a prediction model which can be used to predict the probability that a patient will be adherent, in advance. The model includes four predictors: intention, depression, perceived dyspnea (difficulty in breathing), and the patient-therapist relationship (alliance). To facilitate its use in practice, a web-based calculator is available (https://www.derzis.nu/Calculator/) where healthcare providers can enter the values of the predictors and obtain the probability of adherence. The probability score obtained informs the healthcare provider about the individual patient to support personalized counseling. This tool can aid in decision-making and enable personalized interventions.
Additionally, the researchers developed a protocol to evaluate the safety and effectiveness of increased self-management in patients with COPD, while also assessing the predictive validity of the prediction model. By implementing self-management strategies and utilizing the prediction model, healthcare providers can potentially improve patient outcomes and optimize the use of healthcare resources.
The findings from this research have the potential to benefit both scientific research and practical applications in healthcare settings. The developed tools provide a practical framework for assessing adherence and tailoring interventions to individual patients.