Ground-breaking study shows fitter bodies could lead to fitter economies

• According to the World Health Organization (WHO) insufficient physical inactivity is recognised as one of the leading risk factors for death, posing a global public health problem. Globally, it is estimated that every year, physical inactivity is associated with more than 5 million deaths(1) and is contributing to global healthcare expenditures, as well as lost productivity(2).While many countries have developed national action plans to tackle growing prevalence of physical inactivity, gaps challenges persist with their implementation, often associated with an uncoordinated or underfunded approach.
• Physical inactivity is associated with the onset of a number of different diseases, including cardiovascular disease, stroke, diabetes and cancer (e.g. breast or colon). Physical inactivity is also associated with a mortality and morbidity burden. A healthy population has a positive effect on a country’s economic output (e.g. measured as gross domestic product). Reducing physical inactivity has a positive effect on a country’s labour force by reducing premature mortality and improving rates of sickness absence and levels of presenteeism.
• We believe the implications of this study can be far-reaching, informing policy around the world on issues of public health and state budgets. 

• RAND used a dynamic computational general equilibrium (DSGE) macroeconomic model in this study. DSGEs are used in macroeconomics to explain economic phenomena, such as economic growth and business cycles, and the effects of economic policy, through econometric models based on applied general equilibrium theory and microeconomic principles. The model has been extensively peer reviewed and tested.
• The productivity data stems from data collected as part of Vitality UK’s Britain’s Healthiest Workplace (BHW) Survey, as well as AIA’s Asian Healthiest Workplace Survey. In both, employers are asked about their provision of health and well-being interventions, while employees are asked over 100 questions related to demographic factors (age, gender, education, income), lifestyle and health behaviour (nutrition, smoking habits, physical activity, sleep behaviour), health factors (mental and physical health indicators, chronic and musculoskeletal conditions), as well as workplace productivity and job and life satisfaction. Overall, the sample pool was significant, yielding a collective sample size of 120 143 across seven countries.
• Multivariate regression models were employed to investigate the associations between physical activity and a set of outcome measures, including workplace productivity, quality of life and a set of sleep measures. Depending on the analysis, physical activity is included in the analysis as two variables. First, as a binary indicator taking the value one if the individual regularly achieves the recommended level of physical activity per week (e.g. 150 minutes of moderate or 75 minutes of vigorous physical activity[3]) or zero otherwise. Second, as a continuous variable measuring the MET minutes an individual of physical activity an individual performs every week.
• To measure work engagement, the workplace surveys include questions that build the Utrecht Work Engagement Scale (UWES), which includes three dimensions of work engagement: vigour, dedication, and absorption. The nine questions related to the scale are measured on a 7-point Likert scale.
• To investigate the relationship between physical inactivity and elevated mortality risk, the study uses meta-regression analysis. Meta-regression analysis is a form of systematic review employing a range of statistical methods to synthesise and evaluate specific empirical literature. It helps to better understand the existing research findings on a given empirical effect of interest.
• The study uses the MET unit as a measure for how much energy an individual exerts in performing a physical task. It a measure which adjusts for body size, and is defined as the amount of oxygen consumed while sitting at rest, equal to 3.5 ml oxygen per kg body weight per minute. Alternatively, one MET is defined as 1 kcal/kg/hour and is roughly equivalent to the energy cost of sitting quietly. For comparison, cycling is estimated to equal 3.5-16 METs and running 6-23 METs, depending on intensity.
• On attaining the associations between physical activity to productivity and mortality, these effects are then plugged into the DSGE model to yield the economic outcomes. The analysis on the future economic benefits of changes to physical activity at the population level is based on demographic projections on how the population of each country or region evolves over time. To that end, we demographic projections are generated using input data from the UN[4] and an adapted version of Chapin's cohort-component model, which are implemented as five-year projections using Stata. The cohort-component model starts with the current base population and is categorized for each country region by age, gender and skill level. The base population subsequently evolves by applying assumptions on mortality, fertility and migration rates. The outcome of the model is a projection of the population by (5-year) age, gender and skill groups up to thirty years, applied to each of the 24 countries or regions.

Annual GDP gain by country relative to the status quo (in 2019 USD billions present values), at specific years.

Based on all adults achieving a 20% increase in physical activity, as well as meeting at least the World Health Organization minimum requirements (scenario 3). For instance, assume the population of Argentina begins meeting the conditions of scenario 3 from 2020, it would gain $1.21bn in GDP in 2030 and so forth. 

Annual GDP gain averaged over 30 years, by country relative to status quo (in 2019 USD billions present values) shown for each scenario, quantifying the direct and indirect benefits.