Contributions of changes in physical activity, sedentary time, diet and body weight to changes in cardiometabolic risk.

BACKGROUND: Increased physical activity (PA), reduced time spent sedentary (SED), healthier diet and reduced body weight may all have a positive impact on cardiometabolic risk. The relative importance of change in each of these variables on cardiometabolic risk, however, is unclear. We therefore sought to investigate the relative contributions of changes in PA, SED, diet and body weight on cardiometabolic risk. METHODS: This is a secondary analysis of data collected from the EuroFIT randomised controlled trial, which was a 12-week group-based lifestyle intervention for overweight middle-aged men delivered by coaches in football club stadia aiming to improve PA, SED, diet, and body weight. PA and SED were assessed by accelerometry, diet using the Dietary Instrument for Nutrition Education (DINE). An overall cardiometabolic risk score was derived from combining z-scores for glucose, HbA1c, insulin, lipids and blood pressure. In total, 707 men (from the overall cohort of 1113) with complete data for these variables at baseline and 12-month follow-up were included in the multivariable linear regression analyses. RESULTS: In multivariable analyses, change in number of steps (explaining 5.1% of R2) and dietary factors (less alcohol, fatty and sugary food, and more fruit and vegetables) (together explaining 4.5% of R2), but not changes in standing time or SED, were significantly associated with change in body weight. Changes in number of steps (R2 = 1.7%), fatty food score (R2 = 2.4%), and sugary food score (R2 = 0.4%) were significantly associated with change in cardiometabolic risk score in univariable models. However, in multivariable models which included changes in weight as well as changes in steps and dietary variables, change in weight explained a substantially larger proportion of the change in cardiometabolic risk score, explaining 14.1% of R2 (out of an overall model R2 of 19.0%). When baseline (as well as change) values were also included in the model, 38.8% of R2 for change in cardiometabolic risk score was explained overall, with 14.1% of R2 still explained by change in weight. CONCLUSION: Change in body weight, together with baseline cardiometabolic risk explained most of the change in cardiometabolic risk. Thus, the benefits of increasing physical activity and improving diet on cardiometabolic risk appear to act largely via an effect on changes in body weight. TRIAL REGISTRATION: International Standard Randomised Controlled Trials, ISRCTN-81935608. Registered 06052015. https://www.isrctn.com/ISRCTN81935608?q=&filters=recruitmentCountry:Portugal&sort=&offset=7&totalResults=92&page=1&pageSize=10&searchType=basic-search.

Five-year cost-effectiveness analysis of the European Fans in Training (EuroFIT) physical activity intervention for men versus no intervention.

OBJECTIVES: Increasing physical activity reduces the risk of chronic illness including Type 2 diabetes, cardiovascular disease and certain types of cancer. Lifestyle interventions can increase physical activity but few successfully engage men. This study aims to investigate the 5 year cost-effectiveness of EuroFIT, a program to improve physical activity tailored specifically for male football (soccer) fans compared to a no intervention comparison group. METHODS: We developed a Markov cohort model in which the impact of improving physical activity on five chronic health conditions (colorectal cancer, Type 2 diabetes, coronary heart disease, stroke and depression) and mortality was modelled. We estimated costs from a societal perspective and expressed benefits as quality adjusted life years (QALYs). We obtained data from a 4-country (England, Netherlands, Portugal and Norway) pragmatic randomised controlled trial evaluating EuroFIT, epidemiological and cohort studies, and meta-analyses. We performed deterministic and probabilistic sensitivity analyses to assess the impact of uncertainty in the model's parameter values on the cost-effectiveness results. We used Monte Carlo simulations to estimate uncertainty and presented this using cost-effectiveness acceptability curves (CEACs). We tested the robustness of the base case analysis using five scenario analyses. RESULTS: Average costs over 5 years per person receiving EuroFIT were €14,663 and per person receiving no intervention €14,598. Mean QALYs over 5 years were 4.05 per person for EuroFIT and 4.04 for no intervention. Thus, the average incremental cost per person receiving EuroFIT was €65 compared to no intervention, while the average QALY gain was 0.01. This resulted in an ICER of €5206 per QALY gained. CEACs show that the probability of EuroFIT being cost-effective compared to no intervention is 0.53, 0.56 and 0.58 at thresholds of €10,000, €22,000 and €34,000 per QALY gained, respectively. When using a time horizon of 10 years, the results suggest that EuroFIT is more effective and less expensive compared to (i.e. dominant over) no intervention with a probability of cost-effectiveness of 0.63 at a threshold of €22,000 per QALY gained. CONCLUSIONS: We conclude the EuroFIT intervention is not cost-effective compared to no intervention over a period of 5 years from a societal perspective, but is more effective and less expensive (i.e. dominant) after 10 years. We thus suggest that EuroFIT can potentially improve public health in a cost-effective manner in the long term.