The dimensions and profiles of consumer decision‐making styles of young‐adult Chinese are investigated using a modified model of consumer decision‐making styles and data recently collected from five Chinese universities. The results are then compared with those of similar studies using American and Korean data. While the dimensions of consumer decision‐making styles are similar in these three countries, differences in consumer purchasing power and, maturity of the consumer market may contribute to the differences in consumer decision‐making styles.

Few studies compare alternative measures of land use diversity or mix in relationship to body mass index. We compare four types of diversity measures: entropy scores (measures of equal distributions of walkable land use categories), distances to walkable destinations (parks and transit stops), proxy measures of mixed use (walk to work measures and neighborhood housing ages), and land use categories used in entropy scores. Generalized estimating equations, conducted on 5000 randomly chosen licensed drivers aged 25 to 64 in Salt Lake County, Utah, relate lower BMIs to older neighborhoods, components of a 6-category land use entropy score, and nearby light rail stops. Thus the presence of walkable land uses, rather than their equal mixture, relates to healthy weight.

We expand the search for modifiable features of neighborhood environments that alter obesity risk in two ways. First, we examine residents' access to neighborhood retail food options in combination with neighborhood features that facilitate physical activity. Second, we evaluate neighborhood features for both low income and non-low income neighborhoods (bottom quartile of median neighborhood income vs. the top three quartiles).Our analyses use data from the Utah Population Database merged with U.S. Census data and Dun & Bradstreet business data for Salt Lake County, Utah. Linear regressions for BMI and logistic regressions for the likelihood of being obese are estimated using various measures of the individual's neighborhood food options and walkability features.As expected, walkability indicators of older neighborhoods and neighborhoods where a higher fraction of the population walks to work is related to a lower BMI/obesity risk, although the strength of the effects varies by neighborhood income. Surprisingly, the walkability indicator of neighborhoods with higher intersection density was linked to higher BMI/obesity risk. The expected inverse relationship between the walkability indicator of population density and BMI/obesity risk is found only in low income neighborhoods.We find a strong association between neighborhood retail food options and BMI/obesity risk with the magnitude of the effects again varying by neighborhood income. For individuals living in nonlow income neighborhoods, having one or more convenience stores, full-service restaurants, or fast food restaurants is associated with reduced BMI/obesity risk, compared to having no neighborhood food outlets. The presence of at least one healthy grocery option in low income neighborhoods is

IntroductionLower levels of physical activity among rural relative to urban residents have been suggested as an important contributor to rural–urban health disparity; however, empirical evidence is sparse.MethodsWe examined rural–urban differences in 4 objective physical activity measures (2 intensity thresholds by 2 bout lengths) and 4 subjective measures (total, leisure, household, and transportation) in a nationally representative sample of participants in the National Health and Nutrition Examination Survey (NHANES) 2003–2006. The sample comprised 5,056 adults aged 20 to 75 years. Rural-Urban Commuting Area (RUCA) codes were matched with NHANES subjects to identify urban status and 2 types of rural status. Rural–urban and within–rural differences in physical activity were estimated without and with controls for demographic and socioeconomic variables.ResultsRural residents were less active than urban residents in high-intensity long bout (2,020 counts per minute threshold and 10 miniutes or longer bout length) accelerometer-measured physical activity (42.5 ± 6.2 min/wk vs 55.9 ± 2.8 min/wk) but the difference disappeared with a lower-intensity threshold (760 counts per minute). Rural residents reported more total physical activity than urban residents (438.3 ± 35.3min/wk vs 371.2 ± 12.5 min/wk), with differences primarily attributable to household physical activity. Within rural areas, micropolitan residents were less active than residents in smaller rural areas. Controlling for other variables reduced the size of the differences.ConclusionThe direction and significance of rural–urban difference in physical activity varied by the method of physical activity measurement, likely related to rural residents spending more time in low-intensity household physical activity but less time in high-intensity physical activity. Micropolitan residents were substantially less active than residents in smaller rural areas, indicating that physical activity did not vary unidirectionally with degree of urbanization.

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