Recipient Organization
UNIVERSITY OF VERMONT
(N/A)
BURLINGTON,VT 05405
Performing Department
Community Development and Applied Economics
Non Technical Summary
Obesity continues to be a public health concern in the United States and throughout the world. Obesity is present in every segment of the population. Research has linked obesity with several demographic characteristics, including poverty, education, gender, income, and race. Identification of these demographic segments does not, however, tell us why these groups are more likely to be overweight or obese. Most current obesity research takes a look at a single piece of the issue. Our work augments those studies in that it is more comprehensive in nature and examines a multitude of factors impacting obesity rates and the (inter)relationships between them. The ability to examine population based relationships between time-use patterns, socio-demographic characteristics, food choice and obesity in the context of the U.S. population has historically been limited by data availability. Very little research has focused on time use patterns related to food. What is known is that Americans currently fall short of meeting exercise guidelines. The average American spends less than 20 minutes daily participating in physical activity but over two hours watching television. Time spent in front of the TV is detrimental to present and also future health status. A body of work is developing with regard to time spent in meal production and eating. There is growing concern that knowledge of cooking increases healthy food intake but the United States is a nation that cannot or does not cook. The amount of time spent preparing food and cleaning up since the 1960s has dropped by nearly 50 percent for both working and non-working women. Supermarkets have reacted by increasing their selection of prepared convenience foods. Individuals currently spend 35 percent of their total food eaten at home dollars on packaged, prepared goods. Cluster analysis is a technique that shows promise for analyzing food related time use. In the nutrition literature, it has been used to examine dietary patterns. More recently, it has been used to examine overall time use patterns, physical activity patterns, food expenditure patterns. Connections to and patterns in time use specifically related to food and their relationship to obesity have yet to be studied. This study looks specifically at food related time use in order to better understand the relationship between time use and energy balance. It uses a transdisciplinary approach that more accurately reflects the complexity intrinsic to the obesity issue. Researchers with individual discipline interests can amalgamate their knowledge to create a more comprehensive understanding that will then translate into direct action. Which is exactly what is needed to halt the increasing prevalence of overweight and obesity in the US and worldwide. A variety of integrated solutions targeted toward specific demographic and behavioral segments are necessary and range from changes in individual behaviors related to both food and physical activity, changes in the food and built environment, and changes in public policy.
Animal Health Component
(N/A)
Research Effort Categories
Basic
(N/A)
Applied
(N/A)
Developmental
(N/A)
Goals / Objectives
This study uses data from the American Time Use Survey and the Current Population Survey to explore time use patterns related to food purchasing, preparing meals and cleaning up, eating and drinking, and traveling associated with food consumption. Specifically, we will be determining: 1. How does time allocation impact weight status for each of the following household types: Single headed female households with children (19-30 comprised preliminary data) (Ages 31-50; Ages 51+) Single headed male households with children (Ages 19-30; Ages 31-50; Ages 51+) Single female adult households without children (Ages 19-30; Ages 31-50; Ages 51-62; Ages 62+) Single male headed households without children (Ages 19-30; Ages 31-50; Ages 51-62; Ages 62+) Two adult households with children (Ages 19-30; Ages 31-50; Ages 51+) Two adult households without children with designated head of household (Ages 19-30; Ages 31-50; Ages 51-62; Ages 62+) Food related behavior clusters will be determined separately for the above samples. Normal weight and overweight cohorts within each cluster will then be compared and contrasted against each other to evaluate the effects of time allocation on an individual's weight. 2. How do family composition and competing time demands, including age, education, wage rates, labor force participation, and other household production activities, impact health capital, as measured by a healthy weight. Bi-variate tests of association (ANOVA and Chi-Square analysis depending on the level of measurement of the variables) will be used to determine whether weight status within cluster membership is associated with non-food related time uses and demographic characteristics. 3. Lastly, we will interpret results of the above analyses in the context of the current literature on obesity to formulate policy recommendations. Obesity is a multifaceted problem without a single cause of obesity or is a single solution. This study uses a transdisciplinary approach that will inform a variety of integrated solutions specifically targeting certain demographic and behavioral segments.
Project Methods
Using data from the American Time Use Survey (ATUS), Current Population Survey and Eating and Health Module of the ATUS, we will estimate consumer's demand for a healthy weight. Single activities in the ATUS are aggregated by the BLS into broad groupings of time use. We have reviewed the categorizations and, in some instances, disaggregated the classifications in order to isolate certain time uses and make the ATUS data set appropriate for this type of analysis. Nine food related time use variables will be included in the cluster analysis. The variables, measured in minutes, include total time spent in grocery shopping, food preparation and cleanup; primary eating and drinking; secondary eating; secondary drinking (other than water); and traveling to and from eating and drinking establishments. Also included are total number of primary eating occasion; number of secondary eating occasions; secondary drinking occasions, and minutes spent eating per primary meal. Several subsets will be chosen for time use pattern analysis. Time use patterns overall vary by gender, throughout the lifecycle, and in married couple households versus single headed households. By treating each segment as its own population, we can explore patterns within each demographic constituent. Two Step Cluster Analysis using Schwartz's Baysian Criteria in the Statistical Package for Social Sciences (SPSS 15.0) will be used to identify food related behavior clusters for each group. A data driven approach, cluster analysis can give an indication of complex patterns within a data set, not easily accomplished with economic, sociological, or even social-ecological models, which tend to lead to regression type approaches to empirical analysis. When the relationships among several variables are unknown and several variables may be considered dependent, an approach that examines patterns rather than assumes a causative relationship is appropriate. This study considers the perspectives of a broad contingent of disciplines. Because the ATUS contains data on over 35,000 households (2006-2008), we are able to obtain a large enough sample to conduct the statistical analysis, even when it is broken down into 20 subgroups. Bi-variate tests of association (ANOVA and Chi-Square analysis depending on the level of measurement of the variables) will be used to determine whether cluster membership is associated with non-food related time uses and demographic characteristics. Other time uses measured in the ATUS and included in this study include sleep and sleeplessness, personal care, household activity, work and education, physical activity, socializing, non-active leisure time, screen time, caring for others, volunteering, and travel time not related to eating and drinking. Demographic variables include education, ethnicity, and poverty status. Specific recommendations and intervention strategies will then target each individual sample populations.