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.

Background In the United States (US), first-year university students typically live on campus and purchase a meal plan. In general, meal plans allow the student a set number of meals per week or semester, or unlimited meals. Understanding how students’ use their meal plan, and barriers and facilitators to meal plan use, may help decrease nutrition-related issues.

Methods First-year students’ meal plan and residence information was provided by a large, public, southwestern university for the 2015-2016 academic year. A subset of students (n=619) self-reported their food security status. Logistic generalized estimating equations (GEEs) were used to determine if meal plan purchase and use were associated with food insecurity. Linear GEEs were used to examine several potential reasons for lower meal plan use. Logistic and Linear GEEs were used to determine similarities in meal plan purchase and use for a total of 599 roommate pairs (n=1186 students), and 557 floormates.

Results Students did not use all of the meals available to them; 7% of students did not use their meal plan for an entire month. After controlling for socioeconomic factors, compared to students on unlimited meal plans, students on the cheapest meal plan were more likely to report food insecurity (OR=2.2, 95% CI=1.2, 4.1). In Fall, 26% of students on unlimited meal plans reported food insecurity. Students on the 180 meals/semester meal plan who used fewer meals were more likely to report food insecurity (OR=0.9, 95% CI=0.8, 1.0); after gender stratification this was only evident for males. Students’ meal plan use was lower if the student worked a job (β=-1.3, 95% CI=-2.3, -0.3) and higher when their roommate used their meal plan frequently (β=0.09, 99% CI=0.04, 0.14). Roommates on the same meal plan (OR=1.56, 99% CI=1.28, 1.89) were more likely to use their meals together.

Discussion This study suggests that determining why students are not using their meal plan may be key to minimizing the prevalence of food insecurity on college campuses, and that strategic roommate assignments may result in students’ using their meal plan more frequently. Students’ meal plan information provides objective insights into students’ university transition.

Binge drinking has clear consequences but subtle influences among undergraduate students. While theories of perceived drinking norms and social identity have been determined to be predictive of binge drinking behavior, few studies have tested these influences outside of fraternities, sororities, and athletic teams and little research exists employing social network analysis (SNA) to quantify social ties. In this study, a small, undergraduate dance team was identified to test social identity theory using social network analysis in a peripheral social group. Each member was interviewed for demographic information, personal drinking habits, personal network structure, perceptions of peer drinking within both the personal network and the whole-network (the dance team), and sociometric position within the dance team. Personal network characteristics, whole-network dynamics and perceptions of peer drinking were tested for predictive value of individual binge drinking behavior utilizing binary logistic regression analysis. Results for predictor variables were weakened due to the small sample size (n = 13) and low variability within some constant variables, returning no statistically significant (p < 0.05) independent variables. However, while odds ratios could not be used to construct regression equations, four models were statistically significant overall. Each model was tested again without the constants; no models nor variables were statistically significant. These models indicated, within this sample, that 1) the proportion of a group that adopts binge drinking behavior is predictive of that behavior for the interviewee (in terms of the overall personal network as well as the triads within the personal network); and 2) the perception of the average team member's maximum alcohol intake along with the proportion of the personal network composed of team members is predictive of individual binge drinking behavior. Low variance in the variables and the small sample size warrant further research to test the viability of targeting anti-binge drinking campaigns toward peripheral social groups.

Social structure is the product of the costs and benefits of group living. Dyadic social bonds in female chacma baboons are strong and long-standing, conferring fitness benefits upon both individuals while contributing to a greater social structure. Longitudinal grooming data collected from 2001-2007 from Moremi Game Reserve, Botswana, illuminate social network dynamics of 50 female chacma baboons. Utilizing social network analysis (SNA), we analyzed social structure above the level of the dyad to see if attribute data (age, rank, and number of close female kin) was predictive of network location. Our SNA data was longitudinal, unbalanced, and continuous. We therefore used linear mixed-effects models (LMEs) and respective AIC/BIC values to choose the most likely predictive attributes for each SNA metric. From the chosen LMEs, rank was present most often. High rank predicted a higher frequency of outward grooming, an overall lower number of grooming partners, and a less extensive social network. It appears that high-ranking females have a fewer number of social bonds than low-ranking females, but that they are stronger. Considering that enduring social bonds result in increased offspring longevity, future studies include examining the potential adaptive value of weak, transient, more numerous social bonds.

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.