A collection of scholarly work created by the ASU Food Policy and Environment Research Group under the leadership of Professor Punam Ohri-Vachaspati. The group examines policies, programs, and environments that influence food consumption and physical activity behaviors and health outcomes in disadvantaged populations. We aim to improve the health of children and families through comprehensive policy and environmental approaches. 
 

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Description

Background

The transition from the home to college is a phase in which emerging adults shift toward more unhealthy eating and physical activity patterns, higher body mass indices, thus increasing risk of overweight/obesity. Currently, little is understood about how changing friendship networks shape weight gain behaviors. This paper describes the

Background

The transition from the home to college is a phase in which emerging adults shift toward more unhealthy eating and physical activity patterns, higher body mass indices, thus increasing risk of overweight/obesity. Currently, little is understood about how changing friendship networks shape weight gain behaviors. This paper describes the recruitment, data collection, and data analytic protocols for the SPARC (Social impact of Physical Activity and nutRition in College) study, a longitudinal examination of the mechanisms by which friends and friendship networks influence nutrition and physical activity behaviors and weight gain in the transition to college life.

Methods

The SPARC study aims to follow 1450 university freshmen from a large university over an academic year, collecting data on multiple aspects of friends and friendship networks. Integrating multiple types of data related to student lives, ecological momentary assessments (EMAs) are administered via a cell phone application, devilSPARC. EMAs collected in four 1-week periods (a total of 4 EMA waves) are integrated with linked data from web-based surveys and anthropometric measurements conducted at four times points (for a total of eight data collection periods including EMAs, separated by ~1 month). University databases will provide student card data, allowing integration of both time-dated data on food purchasing, use of physical activity venues, and geographical information system (GIS) locations of these activities relative to other students in their social networks.

Discussion

Findings are intended to guide the development of more effective interventions to enhance behaviors among college students that protect against weight gain during college.

ContributorsBruening, Meg (Author) / Ohri-Vachaspati, Punam (Author) / Brewis, Alexandra (Author) / Laska, Melissa (Author) / Todd, Michael (Author) / Hruschka, Daniel (Author) / Schaefer, David (Author) / Whisner, Corrie M (Author) / Dunton, Genevieve (Author)
Created2016-08-30
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Description

Objective: The Social Ecological Model (SEM) has been used to describe the aetiology of childhood obesity and to develop a framework for prevention. The current paper applies the SEM to data collected at multiple levels, representing different layers of the SEM, and examines the unique and relative contribution of

Objective: The Social Ecological Model (SEM) has been used to describe the aetiology of childhood obesity and to develop a framework for prevention. The current paper applies the SEM to data collected at multiple levels, representing different layers of the SEM, and examines the unique and relative contribution of each layer to children's weight status.

Design: Cross-sectional survey of randomly selected households with children living in low-income diverse communities.

Setting: A telephone survey conducted in 2009-2010 collected information on parental perceptions of their neighbourhoods, and household, parent and child demographic characteristics. Parents provided measured height and weight data for their children. Geocoded data were used to calculate proximity of a child's residence to food and physical activity outlets.

Subjects: Analysis based on 560 children whose parents participated in the survey and provided measured heights and weights.

Results: Multiple logistic regression models were estimated to determine the joint contribution of elements within each layer of the SEM as well as the relative contribution of each layer. Layers of the SEM representing parental perceptions of their neighbourhoods, parent demographics and neighbourhood characteristics made the strongest contributions to predicting whether a child was overweight or obese. Layers of the SEM representing food and physical activity environments made smaller, but still significant, contributions to predicting children's weight status.

Conclusions: The approach used herein supports using the SEM for predicting child weight status and uncovers some of the most promising domains and strategies for childhood obesity prevention that can be used for designing interventions.

ContributorsOhri-Vachaspati, Punam (Author) / DeLia, Derek Michael, 1969- (Author) / DeWeese, Robin (Author) / Crespo, Noe C. (Author) / Todd, Michael (Author) / Yedidia, Michael J., 1946- (Author)
Created2014-11-06