Face to Face or Digitally Delivered First-line Osteoarthritis Treatment?

Summary

Participation Requirements

Description

According to international guidelines, exercise and education should constitute the first-line intervention for people with knee and hip osteoarthritis (OA) and have been shown to be effective in reducing OA symptoms regardless of disease severity. To implement those guidelines, the Better Managemen...

According to international guidelines, exercise and education should constitute the first-line intervention for people with knee and hip osteoarthritis (OA) and have been shown to be effective in reducing OA symptoms regardless of disease severity. To implement those guidelines, the Better Management of Patients with OsteoArthritis (BOA), a face-to-face concept including education and an option to exercise, has been developed and are offered at primary care clinics in Sweden since 2008. However, traditional face to face interventions present barriers, such as limited access and lack of flexibility, which may limit the patients' adherence with the interventions. In an effort to overcome such barriers, a digital self-management program (Joint Academy) that is based on the BOA concept started in 2014. Although, both delivering methods have been reported to reduce OA symptoms in patients with hip and/or knee OA, little is known whether the results of digital interventions are comparable with traditional face-to-face rehabilitation programs. In this retrospective register-based study we will compare the outcomes of the two different modalities of first-line treatment delivery (face-to-face vs digitally) after 3 months program participation using data from the BOA and Joint Academy registers. Main outcome will be self-reported change in pain between baseline and three months follow-up and secondary outcomes will be change in self-reported walking difficulties, willingness for joint surgery and health-related quality of life between baseline and three months follow-up. Statistical analysis In this study we will include all participants in the digital program fulfilling the eligibility criteria outlined below (n >2000). These participants will be matched 1:1 to participants in the BOA register using the propensity scoring approach. The minimum clinically important difference is typically considered to be one unit on a 0-10 NRS scale. To obtain a 95% confidence interval for the between-group difference with a width of at most 0.5 units (i.e. very precise) in a sample with a typical standard deviation of 1.5, with 99% probability, we will need ~630 patients in total. We will use observational data to emulate an equivalence trial comparing the effect on joint pain of a digitally delivered first-line intervention and of an in-person delivered first line intervention for people with OA of the hip or knee. In order to establish equivalence between the interventions the pain change after the intervention should differ of less than 1-point on a 0-10 NRS pain scale. This cut off was selected based on previous work identifying 1-point change as the MCID in people with OA. The main outcome will be analyzed using a propensity score matching approach. We will estimate the propensity score using a logistic regression model, in which we will regress the treatment status on the observed baseline characteristics of the participants. The characteristics to include in the propensity score will be selected using the disjunctive cause criteria, including factors in the analysis identified as causes of treatment allocation(exposure) and/or the pain change (outcome). We will use nearest neighbor matching to select controls (BOA participants) whose propensity score is closest to that of the treated subject. Furthermore, we will use an optimal matching strategy to minimize the total within-pair difference of the propensity score. Within-pair differences in main outcome will be analysed using a paired t-test. Finally, we will adjust the analysis for baseline pain in order to increase the precision of the estimates and minimize the regression to the mean effect

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