This review aims to assess by meta-analysis of randomised controlled trials (RCTs) changes in pain and function when overweight patients with knee osteoarthritis (OA) achieve a weight loss. Systematic searches were performed and reference lists from the retrieved trials were searched. RCTs were enclosed in the systematic review if they explicitly stated diagnosis of knee OA and reported a weight change as the only difference in intervention from the control group. Outcome Measures for Arthritis Clinical Trials III outcome variables were considered for analysis. Effect size (ES) was calculated using RevMan, and meta-regression analyses were performed using weighted estimates from the random effects analyses. Among 35 potential trials identified, four RCTs including five intervention/control groups met our inclusion criteria and provided data from 454 patients. Pooled ES for pain and physical disability were 0.20 (95% CI 0 to 0.39) and 0.23 (0.04 to 0.42) at a weight reduction of 6.1 kg (4.7 to 7.6 kg). Meta-regression analysis showed that disability could be significantly improved when weight was reduced over 5.1%, or at the rate of >0.24% reduction per week. Clinical efficacy on pain reduction was present, although not predictable after weight loss. Meta-regression analysis indicated that physical disability of patients with knee OA and overweight diminished after a moderate weight reduction regime. The analysis supported that a weight loss of >5% should be achieved within a 20-week period--that is, 0.25% per week.

Health policy decisions should be based on national social preferences. In the absence of a set of Danish health preferences, patient outcome studies using the EQ-5D instrument have typically used UK health state valuations. This article describes the development of a Danish EQ-5D value set. Regression modelling was based on Time Trade-Off (TTO) data derived from computer-assisted interviews conducted with 1,332 respondents from the Danish general population. Using a split-sample technique, 46 health states were directly valued by the respondents. Five different model types were tested and compared on statistical and theoretical grounds. Eleven different specifications were then tested for the chosen model type to identify the most appropriate model that had high explanatory power and parameters that were both consistent (positively signed) and statistically significant. An additive random effects model was found to be superior to ordinary least squares, fixed effects, random coefficient and censored Tobit modelling approaches. From the 11 model specifications tested, the TTO3 model (main effects model, without an N3 factor) performed best and was used to generate a Danish set of health state preferences. An additive random effects model appears to adequately generate a Danish set of EQ-5D health state preferences. The model has high explanatory power and produces consistent and significant parameters for EQ-5D dimensions and levels. It is recommended that this value set be used in Danish cost-utility studies using EQ-5D.