Physical Therapist as Primary Assessor for Patients With Knee Pain in Primary Health Care. A Randomized Controlled Study
Physical Therapist as Primary Assessor for Patients With Knee Pain in Primary Health Care. A Randomized Controlled Study
In order to manage the future increase in osteoarthritis consultation, patients with osteoarthritis could be assessed by a physical therapist first, so that other patients with more severe conditions could get faster access to a primary care physician. In Swedish primary care, physicians and physical therapists are primary assessors for patients with suspected knee osteoarthritis. However, it is unclear if there are any differences between these managements in improving health-related quality of life (HrQoL), pain, physical function and self-efficacy.
There are a limited amount of studies about the impact on HrQoL, pain intensity, self-efficacy and physical performance in patients with knee pain being assessed and evaluated by a physical therapist as a primary assessor.
The overall purpose of this study is to evaluate the effects on self-rated HrQoL, pain intensity, self-efficacy and physical performance with either a physical therapist or a physician as primary assessor for patients with knee pain within primary health care.
Problem statements
Which effect does a clinical pathway with a physical therapist as primary assessor for patients with knee pain…
It is expected that this study will show the effects of two different primary assessors for patients with knee pain consulting primary health care. The results could clarify which profession that is most appropriate to be the primary assessor for patients with knee pain in primary health care.
Patient Recruitment Recruiters: Primary care centers.
Screening procedure: Nurses at the primary care centers will get information about the study and the screening protocol from the data collector and project leader. There will be contact persons at each recruiting unit that will be responsible for the protocols and to contact the data collector when a patient fulfills all the criteria for participation. The project leader will have regular contact with the contact persons at the recruiting units. All screening protocols will be sent to the data collector. All participants will get orally and written information about the study from the data collector, and patients will provide written informed consent.
Randomization: Using a computer-generated list of random numbers, participants will be randomly assigned to being assessed, diagnosed and treated either by a physiotherapist or a physician first. A project coordinator is included among the health care providers in the study, but will not be involved in the screening procedure nor the data collection. The project coordinator will manage the sequence generation, allocation concealment, enrolment and assignments of participants and keep the concealed randomization scheme and sequentially numbered, sealed envelopes in a locked cupboard (in the same building where the enrolment will be), only available for the project coordinator. The project coordinator reveals the allocation to the participant shortly after the baseline measurement and to the health care providers.
Participants and health care providers in both groups will be aware of the allocated group, whereas data collector, data analyst and statistician will be kept blinded of allocation until completion of all outcome assessments.
The blinded data collector and analyst (is a physical therapist) whom is not involved in assessing diagnosing and treating patients with knee osteoarthritis while the study is conducted.
Data Collection: Measurements will be collected at baseline (before randomization), at 3- , 6- , and at 12 months follow up.
Data management: All data will be coded and managed according to the Data Protection Act (1998:204), which means that all data will be confidential and no unauthorized will have access to the patient registry. The results will be presented at group level, therefore, no individual information can be identified. Data will be saved for at least 10 years to enable audit.
Sample size: To detect a minimal clinical improvement in health related quality of life of 0.121(SD 0.2) on the EQ5D-index, with a two-sided 5% significance level and a power of 80%, a sample size of 50 patients per group will be necessary, given an anticipated dropout rate of 14%.
Statistical analysis: Data will be analyzed descriptively and presented as numbers and percent, mean and standard deviation or median and 25th to 75th percentiles. The primary outcome will be mean change in HrQoL (EQ5D-index and EQ5D-scale) and secondary outcomes will include mean change in pain intensity (VAS), physical performance (30CST) and self-efficacy (ASES-S). The changes in scores for the outcomes will be constructed by calculating raw differences between baseline and the follow ups. For group comparison the Χ2-test will be used for nominal data and Fischer's exact test for small numbers, parametric student t-test for continuous variables and Mann-Whitney's test for ordinal data and at skewed distributions. Statistical analysis will be made in SPSS Windows and the analysis will be applied with intention-to-treat (ITT). The level of significance will be p< 0.05.
Inclusion Criteria:
Exclusion Criteria: