Modification of physical activity and the prognosis of rheumatoid arthritis
Modifiering av fysisk aktivitet of prognos av reumatoid artrit
Responsible: Helga Westerlind
Introduction
Rheumatoid arthritis (RA), is a chronic inflammatory disease in which the body’s own immune system attacks the tissue in the joints. RA is a lifelong disease that leads to disability, pain and decreased quality of life. Physical activity (PA) was once perceived as potentially harmful for patients with RA, but intervention trials in the past decade have shown that, on the contrary, it is well tolerated in established RA. There is a wealth of literature investigating strategies and barriers for PA among RA patients since positive effects for the overall health is expected in this patient group, similar to the general population. Intervention studies have also indicated that PA may have positive effects on pain and inflammation in RA. Moreover, physical activity is a lifestyle factor that is modifiable and can be influenced by each and every RA patient, and thus offers potential control over the long-term prognosis of RA.
Project details
| Inst f medicin Solna (MedS) | |
| - | |
| Epidemiological study | |
| 1 | |
| Not decided | |
| All data have already been collected and only need to be analyzed | |
| Ethical permit is required and exists |
Supervisor/Contact
Helga Westerlind
0702222646
Helga.Westerlind@ki.se
Contact 2
Aims
The aim of this project is to study how a change in physical activity impacts the disease progression of RA.
Design
Information on PA at three different time points: five years before diagnosis, at diagnosis and one year after, will be obtained from the EIRA questionnaires. Using the data in SRQ, the patients will be followed with regard to disease, pain and treatment, for the first three years after diagnosis.
We will study how PA varies over time in the patients and how this relates to changes in disease activity and chance to reach remission. Through the EIRA questionnaire, we will be able to take into account important confounding factors, like education, BMI, sex and smoking. We will study how the PA changes in relation to disease activity to investigate reversed causation. We will also study the differences in change in disease activity, the chance of reaching disease remission at three years, and the change of disease activity over time. We will also study the subgroup of patients with low inflammation but high pain, to see if PA can influence the non-inflammatory pain.
The student selected to work with this project is required to have a strong interest in analytical methods and willingness to learn programming in the statistical language R.
We will study how PA varies over time in the patients and how this relates to changes in disease activity and chance to reach remission. Through the EIRA questionnaire, we will be able to take into account important confounding factors, like education, BMI, sex and smoking. We will study how the PA changes in relation to disease activity to investigate reversed causation. We will also study the differences in change in disease activity, the chance of reaching disease remission at three years, and the change of disease activity over time. We will also study the subgroup of patients with low inflammation but high pain, to see if PA can influence the non-inflammatory pain.
The student selected to work with this project is required to have a strong interest in analytical methods and willingness to learn programming in the statistical language R.
Material and methods
This project will be carried out using data from the study Epidemiological Investigation of RA (EIRA). The Epidemiological Investigation of RA (EIRA) study has since 1996 collected incident RA cases in Sweden and contains to date over 4000 patients. At diagnosis, the patient is asked to fill out a questionnaire on lifestyle information. Since 2010, the patients participating in EIRA are one year into the disease sent a follow-up questionnaire with questions about the progression of the disease, lifestyle and patient reported outcomes. Through the personal identity number, EIRA has been linked to the Swedish Rheumatology Quality register (SRQ), where real world data from visits to the rheumatologist is available. In total, this gives a cohort of around 1000 newly diagnosed RA patients with clinical and lifestyle information over time.
Information on PA at three different time points: five years before diagnosis, at diagnosis and one year after, will be obtained from the EIRA questionnaires. Using the data in SRQ, the patients will be followed with regard to disease, pain and treatment, for the first three years after diagnosis.
We will study how PA varies over time in the patients and how this relates to changes in disease activity and chance to reach remission. Through the EIRA questionnaire, we will be able to take into account important confounding factors, like education, BMI, sex and smoking. We will study how the PA changes in relation to disease activity to investigate reversed causation. We will also study the differences in change in disease activity, the chance of reaching disease remission at three years, and the change of disease activity over time. We will also study the subgroup of patients with low inflammation but high pain, to see if PA can influence the non-inflammatory pain.
Information on PA at three different time points: five years before diagnosis, at diagnosis and one year after, will be obtained from the EIRA questionnaires. Using the data in SRQ, the patients will be followed with regard to disease, pain and treatment, for the first three years after diagnosis.
We will study how PA varies over time in the patients and how this relates to changes in disease activity and chance to reach remission. Through the EIRA questionnaire, we will be able to take into account important confounding factors, like education, BMI, sex and smoking. We will study how the PA changes in relation to disease activity to investigate reversed causation. We will also study the differences in change in disease activity, the chance of reaching disease remission at three years, and the change of disease activity over time. We will also study the subgroup of patients with low inflammation but high pain, to see if PA can influence the non-inflammatory pain.
Project time schedule
Phase 1: planning and preparation. The student is expected to read up on the litterature, get acquainted with the data, and the statistical methods used. suggested time: 4 weeks.
Phase 2: Practical analysis and half time report. During this period it's suggested to prepare the data set and do some initial demographic analysis, as well as write the half time report. suggested time: 6 weeks.
Phase 3: continued data analysis and final report. Perform the main statistical analysis, interpret the data and write the report. Suggested to spend 6 weeks on the analysis, do a bit or writing on the report at the side, and spend the last 4 weeks focusing on the report.
Phase 2: Practical analysis and half time report. During this period it's suggested to prepare the data set and do some initial demographic analysis, as well as write the half time report. suggested time: 6 weeks.
Phase 3: continued data analysis and final report. Perform the main statistical analysis, interpret the data and write the report. Suggested to spend 6 weeks on the analysis, do a bit or writing on the report at the side, and spend the last 4 weeks focusing on the report.
Collected data/reagents
Physical activity
Clinical parameters relevant for RA
Clinical parameters relevant for RA
Backup plan
The data is available and ready. The statistical methodology is in place. There is support for statistical methods, technical questions, epidemiological methods, and clinical questions available.
Teaching/Supervision activities
Weekly supervision meeting to ensure progress with the project. Weekly research group meetings. Monthly meeting with programming activities.
Resources
A computer for the analysis will be provided by the supervisor.
Miscellaneous
The student selected for this project is expected to either have a background in some sort of programming/analytical language, or a strong desire to learn. The analysis are expected to be performed in R.