Intra-national variation in trends in overweight and leisure time physical activities in The Netherlands since 1980: stratification according to sex, age and urbanisation degree.
ABSTRACT To investigate time trends in overweight and Leisure Time Physical Activities (LTPA) in The Netherlands since 1980. Intra-national differences were examined stratified for sex, age and urbanisation degree.
We used a random sample of about 140,000 respondents aged 20-69 years from the Health Interview Survey (Nethhis) and subsequent Permanent Survey on Living Conditions (POLS). Self-reported data on weight and height and demographic characteristics were gathered through interviews (every year) and data on LTPA were collected by self-administered questionnaires (1990-1997, 2001-2004). Linear regression analysis was performed for trend analyses.
During 1981-2004, mean body mass index (BMI) increased significantly by 1.0 kg/m(2) (average per year=0.05 kg/m(2)). Trends were similar across sex and different degrees of urbanisation, but varied across age groups. In 20-to 39-year-old women, mean BMI increased by 1.7 kg/m(2), which was more than in older age groups (P<or=0.05). With respect to LTPA, no clear trend was observed during 1990-1997 and 2001-2004. The (absence of) trends were similar across sex and urbanisation degrees, but varied across age groups. During 2001-2004, 20-to 39-year-old women spent approximately 150 min/week less on LTPA compared to older women, while this difference was smaller during 1990-1997.
Mean BMI increased more in younger women, which is consistent with the observation that this group spent less time on LTPA during recent years. Although the overall increase in overweight could not be explained by trends in LTPA, the younger women should be considered as a target group for future physical activity interventions. The influence of the 'obesogenic environment' seems to be similar across different degrees of urbanisation.
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ABSTRACT: To examine differences in overweight and obesity of second-generation Turkish, Moroccan and Surinamese migrants v. first-generation migrants and the ethnic Dutch. We also studied the influence of sociodemographic factors on this association. Data were collected in 2008 in a cross-sectional postal and online health survey. Four major Dutch cities. In the survey 42 686 residents aged 16 years and over participated. Data from Dutch (n 3615) and second/first-generation Surinamese (n 230/139), Turkish (n 203/241) and Moroccan (n 172/187) participants aged 16-34 years were analysed using logistic regression with overweight (BMI ≥ 25·0 kg/m2) and obesity (BMI ≥ 30·0 kg/m2) as dependent variables. BMI was calculated from self-reported body height and weight. Sociodemographic variables included sex, age, marital status, educational level, employment status and financial situation. After controlling for age, overweight (including obesity) was more prevalent in most second-generation migrant subgroups compared with the Dutch population, except for Moroccan men. Obesity rates among second-generation migrant men were similar to those among the Dutch. Second-generation migrant women were more often obese than Dutch women. Ethnic differences were partly explained by the lower educational level of second-generation migrants. Differences in overweight between second- and first-generation migrants were only found among Moroccan and Surinamese men. We did not find a converging trend for the overweight and obesity prevalence from second-generation migrants towards the Dutch host population. Therefore, preventive interventions should also focus on second-generation migrants to stop the obesity epidemic.Public Health Nutrition 09/2013; · 2.25 Impact Factor
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ABSTRACT: Objectives. We investigated whether moving to neighborhoods with closer proximity of destinations and greater street connectivity was associated with more walking, a greater probability of meeting the "Every Body Walk!" campaign goals (≥ 150 minutes/week of walking), and reductions in body mass index (BMI). Methods. We linked longitudinal data from 701 participants, who moved between 2 waves of the Multi-Ethnic Study of Atherosclerosis (2004-2012), to a neighborhood walkability measure (Street Smart Walk Score) for each residential location. We used fixed-effects models to estimate if changes in walkability resulting from relocation were associated with simultaneous changes in walking behaviors and BMI. Results. Moving to a location with a 10-point higher Walk Score was associated with a 16.04 minutes per week (95% confidence interval [CI] = 5.13, 29.96) increase in transport walking, 11% higher odds of meeting Every Body Walk! goals through transport walking (adjusted odds ratio = 1.11; 95% CI = 1.02, 1.21), and a 0.06 kilogram per meters squared (95% CI = -0.12, -0.01) reduction in BMI. Change in walkability was not associated with change in leisure walking. Conclusions. Our findings illustrated the potential for neighborhood infrastructure to support health-enhancing behaviors and overall health of people in the United States. (Am J Public Health. Published online ahead of print January 16, 2014: e1-e8. doi:10.2105/AJPH.2013.301773).American Journal of Public Health 01/2014; · 3.93 Impact Factor
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ABSTRACT: The aim of this study was to compare estimates of prevalence of physical activity indicators and associated sociodemographic factors obtained from telephone and face-to-face interviews with adults. Data from a cross-sectional populationbased survey of adults living in Florianópolis, Santa Catarina State, Brazil was compared to data collected through the telephonic system VIGITEL. There was no significant difference between the results from telephone interviews (n = 1,475) and face-to-face interviews (n = 1,720) with respect to prevalence of sufficient leisure time physical activity (19.3% versus 15.5%, respectively), sufficient leisure time and/or commuting physical activity (35.1% versus 29.1%, respectively) and physical inactivity (16.2% versus 12.6%, respectively). Some differences were observed with respect to the sociodemographic factors associated with leisure time and/or commuting physical activity and physical inactivity. The two techniques yielded generally similar results with respect to prevalence and sociodemographic factors associated to physical activity indicators.Cadernos de saúde pública / Ministério da Saúde, Fundação Oswaldo Cruz, Escola Nacional de Saúde Pública 10/2013; 29(10):2119-2129. · 0.83 Impact Factor