Marissa Burgermaster

Intraclass Correlation Coefficients for Obesity Indicators and Energy Balance–Related Behaviors Among New York City Public Elementary Schools

Objective. Sample size and statistical power calculation should consider clustering effects when schools are the unit of randomization in intervention studies. The objective of the current study was to investigate how student outcomes are clustered within schools in an obesity prevention trial. Method. Baseline data from the Food, Health & Choices project were used. Participants were 9- to 13-year-old students enrolled in 20 New York City public schools (n = 1,387). Body mass index (BMI) was calculated based on measures of height and weight, and body fat percentage was measured with a Tanita® body composition analyzer (Model SC-331s). Energy balance–related behaviors were self-reported with a frequency questionnaire. To examine the cluster effects, intraclass correlation coefficients (ICCs) were calculated as school variance over total variance for outcome variables. School-level covariates, percentage students eligible for free and reduced-price lunch, percentage Black or Hispanic, and English language learners were added in the model to examine ICC changes. Results. The ICCs for obesity indicators are: .026 for BMI-percentile, .031 for BMI z-score, .035 for percentage of overweight students, .037 for body fat percentage, and .041 for absolute BMI. The ICC range for the six energy balance–related behaviors are .008 to .044 for fruit and vegetables, .013 to .055 for physical activity, .031 to .052 for recreational screen time, .013 to .091 for sweetened beverages, .033 to .121 for processed packaged snacks, and .020 to .083 for fast food. When school-level covariates were included in the model, ICC changes varied from −95% to 85%. Conclusions. This is the first study reporting ICCs for obesity-related anthropometric and behavioral outcomes among New York City public schools. The results of the study may aid sample size estimation for future school-based cluster randomized controlled trials in similar urban setting and population. Additionally, identifying school-level covariates that can reduce cluster effects is important when analyzing data.

Exploring the Role of Sugar-Sweetened Beverage Consumption in Obesity among New Yorkers Using Propensity Score Matching

BACKGROUNDnResults from clinical trials have shown that sugar-sweetened beverages (SSBs) lead to increased body mass index (BMI) and obesity. This relationship has yet to be explored in observational data for nonclinical populations of adults.nnnOBJECTIVEnTo compare adults who drank 4+ SSBs daily to those who drank 0 in the population of adults in New York City, and to better understand adult risk factors associated with higher daily SSB consumption and BMI.nnnDESIGNnSecondary analysis of cross-sectional data using propensity score matching.nnnPARTICIPANTS/SETTINGnThe 2009 New York City Community Health Survey (N=9,934) was used.nnnMAIN OUTCOME MEASUREnBMI.nnnSTATISTICAL ANALYSESnFor each participant who consumed 4+ SSBs daily, propensity score matching identified matched comparisons who did not drink any SSBs. BMI in unadjusted and matched pairs was tested using t tests. A post hoc analysis compared features of those likely to drink SSBs and those not likely to drink SSBs.nnnRESULTSnIn unmatched analyses, participants who consumed 4+ SSBs daily (n=475) had higher BMI than those who consumed 0 SSBs (n=3,818; BMI difference=1.4±0.29; txa0value=4.81; P<0.001); however, when compared with similar participants using nearest neighbor with replacement matching (n=1,062), the difference between those who consumed 4+ SSBs daily and those who consumed none decreased (BMI difference=0.37±0.36; t value=1.01; P=0.32). Analyses also indicated that those likely toxa0drink SSBs and those unlikely to drink SSBs differed in several important characteristics, including sex, age, race, ethnicity, socioeconomic status, education, diet, and exercise.nnnCONCLUSIONSnThe data preclude strong causal conclusions about the role of SSB in obesity. However, our results suggest that there is a subset of participants demographically and behaviorally similar with higher BMI regardless of their self-reported SSB intake. In addition to targeting SSBs, public health policies and programs should identify and address other modifiable aspects of this profile and tailor approaches to the groups identified to be most affected by high BMI.

A visual analytics approach for pattern-recognition in patient-generated data

Abstract Objective To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload. Methods Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation. Results Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, pu2009=u2009.17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, pu2009=u2009.027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering. Conclusions Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.

The use of social media in nutrition interventions for adolescents and young adults—A systematic review

OBJECTIVEnSocial media is a potentially engaging way to support adolescents and young adults in maintaining healthy diets and learning about nutrition. This review identifies interventions that use social media to promote nutrition, examines their content and features, and evaluates the evidence for the use of such platforms among these groups.nnnMATERIAL AND METHODSnWe conducted a systematic search of 5 databases (PubMed, CINAHL, EMBASE, PsycINFO, and ACM Digital Library) for studies that included: 1) adolescents and/or young adults (ages 10-19; ages 18-25); 2) a nutrition education or behavior change intervention component, or outcomes related to nutrition knowledge or dietary changes; and 3) a social media component that allowed users to communicate or share information with peers.nnnRESULTSn16 articles were identified that included a social media component in a nutrition-related intervention for adolescents or young adults. Interventions included features in 7 categories: social media; communication; tracking health; education; tailoring; social support; and gamification. 11 out of the 16 studies had at least one significant nutrition-related clinical or behavioral outcome.nnnCONCLUSIONnSocial media is a promising feature for nutrition interventions for adolescents and young adults. A limited number of studies were identified that included social media. A majority of the identified studies had positive outcomes. We found that most studies utilized only basic social media features, did not evaluate the efficacy of social media components, and did not differentiate between the efficacy of social media compared to other delivery mechanisms.

Predictors of School Garden Integration: Factors Critical to Gardening Success in New York City:

The purpose of this study was to determine the level of integration of school gardens and identify factors that predict integration. 211 New York City schools completed a survey that collected demographic information and utilized the School Garden Integration Scale. A mean garden integration score was calculated, and multiple regression analysis was conducted to determine independent predictors of integration and assess relationships between individual integration characteristics and budget. The average integration score was 34.1 (of 57 points) and ranged from 8 to 53. Operating budget had significant influence on integration score, controlling for all other factors (p < .0001). Partner organizations, evaluation/feedback, planning the physical space, and characteristics of the physical space were positively and significantly related to budget. The results of this study indicate that any garden can become well integrated, as budget is a modifiable factor. When adequate funding is secured, a well-integrated garden may be established with proper planning and sound implementation.