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Exploring the Effect of Participation in Sports on the Risk of Overweight

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Abstract

A growing number of academic studies on public health have highlighted that participation in sports can help prevent people from becoming overweight. Estimates of the effect of sports on the risk of overweight based on sample surveys have primarily controlled for a number of socio-behavioural and demographic observed covariates and neglected to control for both observable and unobservable confounders. The purpose of our study is to estimate the effects of sport activity on risk of overweight by accounting for both confounders. We use microdata collected by the Italian National Statistical Office for 2011 during the Daily Living Conditions Survey of Italian households. The sports-overweight relationship is estimated after controlling for a number of observables using univariate probit modelling. A recursive bivariate probit approach is used to account for both observed and unobserved confounders (better known as endogeneity). Both modelling strategies are performed within a semiparametric and parametric framework. The main findings of the proposed sensitivity analysis suggest that participation in sport activities has a statistically significant negative effect on the risk of overweight and that endogeneity might not be as important as initially suspected. We conclude the presentation of our results by suggesting some policy recommendations and future extensions of the analysis.

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Notes

  1. 1 According to the Council of Europe, sport includes ‘all forms of physical activity which, through casual or organised participation, aim at expressing or improving physical fitness and mental well-being, forming social relationships or obtaining results in competition at all levels’ (Council of Europe, 1992).

  2. 2 Although age and years of education are not the focus of our study, an incorrect categorisation of these variables (or the specification of a linear relationship between age and years of education on overweight when this assumption is not supported by the data) can lead to a biased estimate of the effect of sport activity on the risk of overweight.

  3. 3 For completeness, the estimate parameters of the remaining variables are reported in Appendix A (see the third column of Tables 4 and 5 and Figs. 3 and 5)

    Table 2 β and ATT estimates (in %) for men and women in the study of the connection between sports and overweight. In parentheses are the 95 % Bayesian ’confidence’ intervals the \(\widehat {ATT}\) for all methods (by men and women), which were obtained using 1,000 coefficient vectors simulated from the posterior distribution of the estimated model parameters. SRBP-ER and SRBP-noER are semiparametric recursive bivariate probit models with and without exclusion restrictions, respectively. PRBP-ER and PRBP-noER are parametric recursive bivariate probit models with and without exclusion restrictions, respectively. S-probit and Probit are semiparametric and parametric probit models, respectively.
    Full size table

  4. 4 From a methodological viewpoint, some empirical studies observed that the use of S-Probit and a propensity score weighting approach lead to similar results in terms of \(\widehat {ATT}\) (for further details, please refers to Radice et al. 2013; Zanin et al. 2013)

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Acknowledgments

We would like to thank ISTAT for granting us permission to use the 2011 survey on the ‘Daily Living Conditions’ of the Italian population, Giampiero Marra for his helpful comments after reading an early draft of the work, and two anonymous reviewers for the suggestions that have helped improve the presentation of the article.

The views expressed in the article are those of the author and do not imply any responsibility by the institution of affiliation.

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    Luca Zanin

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Appendices

Appendix A:

Table 4 Parametric estimates of β and observed confounders for the sample of men
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Table 5 Parametric estimates of β and observed confounders of models for the sample of women
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Fig. 4
figure 4

Estimated smooth components for the continuous variable included in model (4) for the sample of women. The results are reported on the scale of the respective linear predictors. The estimated degrees of freedom of the smooth curves are reported on the y-axis of each graph. Dashed lines represent the 95 % Bayesian ‘confidence’ intervals, and the ‘rug plot’ at the bottom of each graph is used to indicate the covariate values. In testing smooth components for equality to zero, we obtain p-values lower than 5 %

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Fig. 5
figure 5

Estimated smooth components for the continuous variable included in model (3) for the sample of women. The results are reported on the scale of the respective linear predictors. The estimated degrees of freedom of the smooth curves are reported on the y-axis of each graph. Dashed lines represent the 95 % Bayesian ‘confidence’ intervals, and the ‘rug plot’ at the bottom of each graph is used to indicate the covariate values. In testing smooth components for equality to zero, we obtain p-values lower than 5 %

Full size image

Appendix B:

Estimating the ATT using SRBP-ER

The semiparametric recursive bivariate probit model was fitted using the function SemiParBIVProbit:

where as.factor() is used to specify categorical variables. s() specifies the smooth components represented by penalised thin plate regression splines (bs = "tp") with basis dimensions k equal to 10 and 6 for age and education, respectively, and penalties based on second-order derivatives (m = 2). The number of basis dimensions was determined using a sensitivity analysis. Multiple smoothing parameter estimation is achieved using the approximate UBRE, and pr.tol=1e-06 is the tolerance used to judge the algorithm convergence. The function LM.bpm() is employed before fitting the recursive bivariate probit model to test the null hypothesis of no endogeneity.

The ATT and associated 95 % CIs were estimated as follows:

AT (SRBP, eq=2, nm.bin="sports", n.sim=1000, E=FALSE, treat=TRUE)

where the treatment effect of sports (nm.bin="sports") on the probability of being overweight was calculated considering only the individuals who participate in sports (E=FALSE, treat=TRUE), for the equation of interest (eq = 2). The number of simulated draws (n.sim) from the posterior distribution of the estimated model parameters for calculating the 95 % CIs for the estimated ATT was set to 1000. The SRBP-ER and SRBP-noER models were fitted using the same code fragments as above, except the first equation of the SRBP-ER included the IV. Classic semiparametric univariate probit models were also fitted using the same thin plate regression spline settings discussed above. The results were extracted using summary(SRBP$gam2). Further details can be found in the work of Marra and Radice (2013).

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Zanin, L. Exploring the Effect of Participation in Sports on the Risk of Overweight. Applied Research Quality Life 10, 381–404 (2015). https://doi.org/10.1007/s11482-014-9317-3

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