The mitigating influence of time preference on the relation between smoking and BMI scores

Robb, Cliff A.; Huston, Sandra J.; Finke, Michael

doi:10.1038/ijo.2008.151

Cited by 14 publications

(10 citation statements)

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“…The covariates used as explanatory variables included sets of socioeconomic and demographic variables, diet‐ and physical activity–related variables, and health status and health‐seeking behavior variables. The chosen variables have been associated with explaining BMI in previous studies (1,2,6,7,8,11,12), and all regressions were estimated using the full set of variables described below.…”

Section: Methods and Proceduresmentioning

confidence: 99%

“…Nutrition knowledge is likely to affect an individual's ability to control their weight (11). A variable proxying nutrition knowledge was specified in the form of knowledge of Chinese dietary guidelines (0: do not have knowledge; 1: have knowledge).…”

Section: Methods and Proceduresmentioning

confidence: 99%

“…The self‐rated current health status of the individual was also included as a dummy regressor (0 = poor or fair health; 1 = good health). Smoking has been shown in several studies to have a negative effect on body weight and BMI (11,12).…”

Section: Methods and Proceduresmentioning

confidence: 99%

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Obesity in China: The Differential Impacts of Covariates Along the BMI Distribution

Shankar¹

2010

Obesity

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Growing prosperity and changing diets have contributed to a surge in obesity prevalence in China. Previous research has investigated the relationships between BMI and several socioeconomic, diet‐related, and health‐related variables in China. This study proposes that such relationships are likely to differ along the conditional BMI distribution, and seeks to investigate such quantile‐dependent variation in effects. Special attention is paid to how variables affect the upper tail of the conditional BMI distribution where overweight and obesity concerns are more acute. Quantile regressions (QRs) and ordinary least squares (OLS) regressions are estimated. The sample consists of 3,407 adult individuals aged 20–45 who participated in the China Health and Nutrition Survey (CHNS), 2006. Substantial cross quantile variation is observed in the relationships between several key variables and BMI. The QR shows that the relationship between energy intake and BMI is largely insignificant in the lower and middle quantiles, whereas the upper quantiles show a positive and significant effect substantially larger than predicted by the least squares regression and by previous studies. This implies that a food‐based strategy aimed at limiting energy intake can be an effective way to fight obesity in China. The negative association between smoking and BMI, on the other hand, is found largely to hold only in the lower and middle quantiles, with the upper tail relatively unaffected by smoking status. Thus, smoking cessation policies may not exacerbate obesity.

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Section: Methods and Proceduresmentioning

confidence: 99%

Section: Methods and Proceduresmentioning

confidence: 99%

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Obesity in China: The Differential Impacts of Covariates Along the BMI Distribution

Shankar¹

2010

Obesity

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“…The vast majority of longitudinal and cross-sectional work finds an inverse correlation between smoking and BMI (Klesges et al 1989), though much of this work fails to account for the mitigating effects of time preferences. Since time preference has been found to be an important predictor of BMI (Robb et al 2008), exclusion of time preferences in studies examining the link between BMI and smoking can be confronted with omitted variable bias.…”

Section: Smoking and Educationmentioning

confidence: 99%

Time preferences and health behaviour: a review

2013

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In this paper, we review published studies to assess the influence of time preferences on human health behaviour. Our review indicates that elicited discount rates for health have been found to be higher than those for money in both the social and private context. We discuss the importance of discount rates for public policy since high time discount rates can contribute to governmental emphasis on acute health care, rather than preventive health care. We then examine how time preferences interrelate with specific health concerns such as smoking or obesity. We find that even when time preferences are elicited in the monetary domain, they can be successful in predicting smoking cessation and likewise for obesity. We also discuss how time preferences relate with teen risk taking behavior. JEL codes: D91, I0

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“…Previous knowledge reports an inverse relationship between BMI and smoking habits in individuals with normal weight (Lin et al 2004; Robb et al 2008; Pisinger and Jorgensen 2007); however, it has been demonstrated that in patients with overweight and obesity, there is a positive correlation with BMI (Chatkin et al 2010) and that smoking leads to the development of insulin resistance and metabolic syndrome (Cena et al 2011) with higher waist circumference as well as BMI in heavy smokers. (Cena et al 2013).…”

Section: Introductionmentioning

confidence: 96%

Evaluation of eating habits and lifestyle in patients with obesity before and after bariatric surgery: a single Italian center experience

et al. 2016

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BackgroundThe study evaluated and compared the eating habits and lifestyle of patients with moderate to severe obesity who have undergone Roux-en-Y Gastric Bypass (RYGB) and Sleeve Gastrectomy (SG).MethodsFood frequency (FF), food habits (FH), physical activity and life style (PA) as well as smoking habits (SH) were analyzed in 50 RYGB (25 M; aged: 24–64) and 50 SG patients (25 M; aged: 22–63) by means of a validated questionnaire, before (T0) and 6 months (T1) post bariatric surgery. A score for each section (FF, FH, PA, SH) was calculated.ResultsANOVA analysis (age/sex adjusted): FF and FH scores improved at T1 (RYGB and SG: p < 0.001); PA score improved but not significantly; SH score did not change at T1 neither in RYGB nor in SG. Mixed models: FF and PA scores did not correlate with age, gender, weight, BMI, neither in RYGB nor in SG; FH score was negatively correlated both with weight (RYGB: p = 0.002) and BMI (SG: p = 0.003); SH score was positively correlated with age, in SG (p = 0.002); the correlation was stronger in females than in males (p = 0.004).ConclusionsAlthough dietary habits improved, patients did not change their physical activity level or their smoking habits. Patients should receive adequate lifestyle counseling to ensure the maximal benefit from bariatric surgery.

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The mitigating influence of time preference on the relation between smoking and BMI scores

Cited by 14 publications

References 98 publications

Obesity in China: The Differential Impacts of Covariates Along the BMI Distribution

Obesity in China: The Differential Impacts of Covariates Along the BMI Distribution

Time preferences and health behaviour: a review

Evaluation of eating habits and lifestyle in patients with obesity before and after bariatric surgery: a single Italian center experience

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