Publications by authors named "Caroline Glagola Dunn"

7 Publications

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Leveraging Implementation Science in the Public Health Response to COVID-19 : Child Food Insecurity and Federal Nutrition Assistance Programs.

Public Health Rep 2020 Nov/Dec;135(6):728-736. Epub 2020 Oct 8.

25980 Georgetown University Law Center, Washington, DC, USA.

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http://dx.doi.org/10.1177/0033354920959285DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649993PMC
November 2020

School Closures During COVID-19: Opportunities for Innovation in Meal Service.

Am J Public Health 2020 11 17;110(11):1635-1643. Epub 2020 Sep 17.

Eliza W. Kinsey is with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. Amelie A. Hecht is with the Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. Caroline Glagola Dunn is with the Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA. Ronli Levi and Hilary K. Seligman are with the Department of Medicine and the Center for Vulnerable Populations, University of California, San Francisco. Margaret A. Read, Courtney Smith, and Pamela Niesen are with Share Our Strength, No Kid Hungry Campaign, Washington, DC. Erin R. Hager is with the Department of Pediatrics and the Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore.

In 2019, the National School Lunch Program and School Breakfast Program served approximately 15 million breakfasts and 30 million lunches daily at low or no cost to students.Access to these meals has been disrupted as a result of long-term school closures related to the COVID-19 pandemic, potentially decreasing both student nutrient intake and household food security. By the week of March 23, 2020, all states had mandated statewide school closures as a result of the pandemic, and the number of weekly missed breakfasts and lunches served at school reached a peak of approximately 169.6 million; this weekly estimate remained steady through the final week of April.We highlight strategies that states and school districts are using to replace these missed meals, including a case study from Maryland and the US Department of Agriculture waivers that, in many cases, have introduced flexibility to allow for innovation. Also, we explore lessons learned from the pandemic with the goal of informing and strengthening future school nutrition policies for out-of-school time, such as over the summer.
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http://dx.doi.org/10.2105/AJPH.2020.305875DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542295PMC
November 2020

Exploring Dietary Behavior Differences among Children by Race/Ethnicity and Socioeconomic Status.

J Sch Health 2020 08 17;90(8):658-664. Epub 2020 Jun 17.

Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Room 529, Columbia, SC, 29201.

Background: In Spartanburg County, SC, nearly 33.7% of children are overweight or obese. The purpose of this study was to investigate differences in eating behavior of youth by race/ethnicity and socioeconomic status.

Methods: Students (N = 997) in 4th to 5th grades completed the School Nutrition and Physical Activity Survey. School databases categorized students as either White or racial/ethnic minority and free/reduced or full paid lunch status. Dietary behaviors included 13 composite measures: unhealthy proteins, healthy proteins, dairy, refined grains, whole grains, vegetables, fruit, fried snacks, sugar-sweetened beverages, sweets, and consumption of a breakfast, evening, and/or restaurant meal. Logistic regression, controlling for sex, was used to analyze differences in consumption for each nutrition variable.

Results: Minority youth were less likely to consume healthy proteins (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.55-0.92) and more likely to eat at a restaurant (OR = 1.32, 95% CI = 1.02-1.70) compared to white youth. Lower socioeconomic status youth were less likely to eat an evening meal compared to higher socioeconomic status youth (OR = 0.59, 95% CI = 0.39-0.89).

Conclusions: Differences in dietary behaviors may result from food accessibility and insecurity in minority and/or low-income neighborhoods. Future research should explore policy strategies that can help ensure all youth maintain healthy eating habits and weight status.
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http://dx.doi.org/10.1111/josh.12915DOI Listing
August 2020

Calorie and nutrient trends in large U.S. chain restaurants, 2012-2018.

PLoS One 2020 10;15(2):e0228891. Epub 2020 Feb 10.

Department of Population Medicine, Harvard Medical School, Boston, MA, United States of America.

Introduction: Large chain restaurants reduced calories in their newly-introduced menu items from 2012 to 2015. The objective of this study was to provide updated calorie trends through 2018 and examine trends in the macronutrient composition of menu items across this time period.

Methods And Findings: Data were obtained from the MenuStat project and include 66 of the 100 largest revenue generating U.S. chain restaurants (N = 28,238 items) that had data available in all years from 2012 to 2018. Generalized linear models were used to examine per-item calorie and nutrient changes (saturated fat, trans fat, unsaturated fat, sugar, non-sugar carbohydrates, protein, sodium) among (1) items on the menu in all years (common items) and (2) newly introduced items (2013-2018). Overall, there were no significant changes in calories or nutrients among common items from 2012 to 2018. Among all newly introduced items, calories (-120 kcals, -25%, p = 0.01; p-for-trend = 0.02), saturated fat (-3.4g, -41%, p<0.01, p-for-trend = 0.06), unsaturated fat (-4.5g, -37%, p = 0.02; p-for-trend = 0.04), non-sugar carbohydrates (-10.3g, -40%, p = 0.02, p-for-trend = 0.69), and protein (-4.3g, -25%, p = 0.04, p-for-trend = 0.02) declined.

Conclusion: Newly introduced menu items in large chain restaurants have continued to decline in calories through 2018, which may help to reduce calorie intake. Other changes in macronutrient content were sporadic and not clearly toward improved dietary quality.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228891PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7010289PMC
May 2020

Church Leaders' Views of Obesity Prevention Efforts for Children and Youth.

J Nutr Educ Behav 2020 03 31;52(3):259-269. Epub 2019 Oct 31.

Department of Health Promotion, Education, and Behavior, University of South Carolina Arnold School of Public Health, Columbia, SC.

Objective: To examine church leaders' views on the role of faith-based organizations in promoting healthy eating and physical activity in children.

Design: Qualitative research using semi-structured in-depth interviews.

Setting: South Carolina.

Participants: Leaders (n = 26) from United Methodist churches (n = 20).

Phenomenon Of Interest: Perceptions of health promotion efforts for children in faith-based settings, including primary health concerns, perceived opportunities, partnerships, and the relationship of these efforts to the overall church mission.

Analysis: Interviews were transcribed verbatim and coded using a constant comparative method.

Results: Five themes emerged related to (1) multiple concerns about health issues facing children; (2) existing church structures influencing health behaviors; (3) potential partnerships to address children's health; (4) importance of role models; and (5) the need for a tailored approach.

Conclusions And Implications: Church leaders viewed childhood health behaviors as an important area of concern for the church and identified links between physical and spiritual health. They identified multiple existing and potential organizational and community structures as important in improving healthy eating and physical activity. Faith-based organizations can play an important role in developing and delivering health programming for children but desired assistance through partnerships with subject matter experts.
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http://dx.doi.org/10.1016/j.jneb.2019.09.019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064414PMC
March 2020

Dietary Self-Monitoring Through Calorie Tracking but Not Through a Digital Photography App Is Associated with Significant Weight Loss: The 2SMART Pilot Study-A 6-Month Randomized Trial.

J Acad Nutr Diet 2019 09 30;119(9):1525-1532. Epub 2019 May 30.

Background: Dietary self-monitoring (DSM) of foods and beverages is associated with weight loss in behavioral interventions; however, DSM may be burdensome, and adherence may decrease over time. Novel methods of DSM, including apps that track food using photographs, may decrease burden, increase DSM adherence, and improve weight loss.

Objective: The objective was to test a mobile photo DSM app compared to a calorie-tracking DSM app on tracking frequency and weight loss in a remotely delivered behavioral weight-loss intervention.

Design: This was a 6-month (October 2016 to April 2017) randomized trial.

Participants/setting: Participants were adults (n=41) classified as overweight or obese (body mass index 25 to 49.9) from South Carolina.

Intervention: Participants received remotely delivered twice-weekly behavioral weight-loss podcasts and tracked diet using a calorie-tracking DSM app (Calorie Group) or a photo DSM app (Photo Group).

Main Outcome Measures: Main outcomes were the number of days diet was tracked, podcasts downloaded, and weight change at 6 weeks and 6 months.

Statistical Analyses: Researchers used nonparametric Wilcoxon rank sum tests and χ analysis to test for differences between groups at baseline; repeated-measures models to estimate weight change and Spearman correlations to determine relationships between DSM frequency, podcasts downloaded, and weight change at 6 months.

Results: There were no differences between groups for the number of days that diet was recorded (P=0.18), which was low overall (<30% of days) but was statistically significantly and strongly correlated with weight change for all participants pooled (r=0.63; P<0.001) and for the calorie tracking group (r=0.70; P=0.004), but not the photo tracking group (r=0.51; P=0.06). Participants in both groups had significant weight loss at 6 months (Photo Group, -2.5±0.9 kg; P=0.008; Calorie Group -2.4±0.9 kg; P=0.007), with no differences between groups at either 6 weeks (P=0.66) or at 6 months (P=0.74).

Conclusions: As part of a remotely delivered weight loss intervention, frequency of DSM was significantly associated with overall weight loss for participants using a calorie DSM app but not a photo DSM app. DSM was low regardless of group and weight loss was significant, although minimal. Increasing user engagement with any DSM may be important to increase self-monitoring and improve weight loss.
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http://dx.doi.org/10.1016/j.jand.2019.03.013DOI Listing
September 2019

Defining Adherence to Mobile Dietary Self-Monitoring and Assessing Tracking Over Time: Tracking at Least Two Eating Occasions per Day Is Best Marker of Adherence within Two Different Mobile Health Randomized Weight Loss Interventions.

J Acad Nutr Diet 2019 09 30;119(9):1516-1524. Epub 2019 May 30.

Background: Mobile dietary self-monitoring methods allow for objective assessment of adherence to self-monitoring; however, the best way to define self-monitoring adherence is not known.

Objective: The objective was to identify the best criteria for defining adherence to dietary self-monitoring with mobile devices when predicting weight loss.

Design: This was a secondary data analysis from two 6-month randomized trials: Dietary Intervention to Enhance Tracking with Mobile Devices (n=42 calorie tracking app or n=39 wearable Bite Counter device) and Self-Monitoring Assessment in Real Time (n=20 kcal tracking app or n=23 photo meal app).

Participants/setting: Adults (n=124; mean body mass index=34.7±5.6) participated in one of two remotely delivered weight-loss interventions at a southeastern university between 2015 and 2017.

Intervention: All participants received the same behavioral weight loss information via twice-weekly podcasts. Participants were randomly assigned to a specific diet tracking method.

Main Outcome Measures: Seven methods of tracking adherence to self-monitoring (eg, number of days tracked, and number of eating occasions tracked) were examined, as was weight loss at 6 months.

Statistical Analyses Performed: Linear regression models estimated the strength of association (R) between each method of tracking adherence and weight loss, adjusting for age and sex.

Results: Among all study completers combined (N=91), adherence defined as the overall number of days participants tracked at least two eating occasions explained the most variance in weight loss at 6 months (R=0.27; P<0.001). Self-monitoring declined over time; all examined adherence methods had fewer than half the sample still tracking after Week 10.

Conclusions: Using the total number of days at least two eating occasions are tracked using a mobile self-monitoring method may be the best way to assess self-monitoring adherence during weight loss interventions. This study shows that self-monitoring rates decline quickly and elucidates potential times for early interventions to stop the reductions in self-monitoring.
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http://dx.doi.org/10.1016/j.jand.2019.03.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6856872PMC
September 2019