Improving Health Behaviors Through Community Engagement: Challenge for a Healthier Louisiana

Abstract

There is increasing recognition that community-based approaches may have merit in improving physical activity and healthy eating behaviors. The “Challenge for a Healthier Louisiana” program supported twelve projects that addressed the root causes of obesity through integrated community-level changes. Partnerships provided community-based obesity prevention by promoting healthier eating and/or physical activity through enhanced infrastructure, policy changes, and programming. To evaluate whether the program resulted in changes in healthy eating and/or physical activity among adults, surveys were conducted pre- and postintervention among participants. Participants who were exposed to physical activity programs were more likely to adopt the consumption of fruits (odds ratio = 2.0; 95% confidence interval [1.1, 3.6]), were more likely to eat vegetables once per day (p = .028), and were more likely to participate in physical activity (p = .053). Participants who were exposed to healthy eating programs were more likely to eat fruit once per day (p = .035), were more likely to eat vegetables at least once per day (p = .008), and were more likely to participate in physical activity (p = .018). In conclusion, there is some indication that the Challenge for a Healthier Louisiana program produced changes in health behaviors among program participants; however, the sustainability of these changes will require further evaluation.

Keywords

obesity collective action physical activity dietary intake

Introduction

The obesity epidemic is a global public health challenge. In the United States, approximately 38% of adults and 17% of children and adolescents are obese (Flegal, Kruszon-Moran, Carroll, Fryar, & Ogden, 2016; Ogden et al., 2016). Many obesity interventions have focused on individual-level behavior change (with varying levels of intensity) involving healthy eating (HE) and physical activity (PA). Indeed, comprehensive, intensive lifestyle interventions are recommended as front-line therapy for the clinical treatment and management of obesity (Jensen & Ryan, 2014). However, to date interventions that target individuals and their behaviors have not been successful at addressing obesity at a population level.

In recent years, obesity prevention efforts have shifted from a predominant focus on the individual to one that also promotes change at higher levels within a social-ecological framework (Ahrens et al., 2011; Koplan, Liverman, & Kraak, 2005; Kumanyika et al., 2008). Recent efforts include the Communities Putting Prevention to Work program (2010-2012), in which the U.S. Centers for Disease Control and Prevention invested nearly $400 million across 50 communities to support changes in community and school policies, systems, organizations, and environments to support healthy behaviors related to obesity and tobacco use (Soler et al., 2016). From 2006 to 2012, the European Commission funded a multilevel intervention (i.e., community, family, school, and individual intervention components) to prevent obesity in young children across eight European countries (the Identification and prevention of Dietary- and lifestyle-induced health EFfects In Children and infantS, or IDEFICS, study) (Ahrens et al., 2011). Despite a comprehensive obesity prevention intervention that targeted multiple levels of influence, the study did not achieve significant changes in adiposity outcomes (De Henauw et al., 2015). In retrospect, the lack of consistent relationships between the targeted obesogenic behaviors and adiposity reported in the literature may have predicted the lack of intervention effects (Baranowski & Lytle, 2015). Another recent study evaluated the Healthy Alberta Communities Project, a 3-year (2006-2009) community-based intervention that helped community stakeholders identify and implement environmental solutions to obesity and chronic disease prevention (Raine et al., 2013). Unfortunately, the results demonstrated no significant changes in body mass index (BMI) or related health behaviors. Despite these efforts, little is known about the effectiveness of community efforts to tackle obesity, and communities typically struggle with how to address obesity and evaluate the impact of their efforts (Ahrens et al., 2011).

Current studies on community-based obesity prevention were preceded by similar efforts targeting cardiovascular disease reduction. During the 1970s and 1980s, several community intervention studies were conducted in the United States, including the Stanford Three Community Study (Fortmann, Williams, Hulley, Haskell, & Farquhar, 1981), Stanford Five-City Study (Taylor et al., 1991), Pawtucket Heart Health Program (Carleton, Lasater, Assaf, Feldman, & McKinlay, 1995), The Minnesota Heart Health Program (Jeffery et al., 1995), and the Heart to Heart Project in South Carolina (R. M. Goodman, Wheeler, & Lee, 1995). In general, the effects of these efforts on measures of obesity were very small and appear to be overwhelmed by the strong secular trends of increasing obesity in the United States during this period (Reeder & Katzmarzyk, 2007). The most positive results were obtained in the Heart to Heart Program, where community-wide campaigns resulted in a smaller increase in obesity in the intervention city compared with the control city (p < .0002) (R. M. Goodman et al., 1995).

Recently, a Louisiana program provided significant funding for community-developed obesity-prevention programs that were required to include components addressing change at higher levels within a social-ecological framework (e.g., policies, systems, and environments) and, hence, the root causes of obesity, PA, and dietary intake. Given the paucity of data on outcomes evaluation of community obesity prevention efforts, the aim of this study is to evaluate the effects of this state-wide, community-based prevention program on behavioral targets of HE and PA.

Method

From 2012 to 2015, the Blue Cross and Blue Shield of Louisiana Foundation (BCBSLAF), a foundation associated with a private, nonprofit health insurance company operating in Louisiana, initiated the Challenge for a Healthier Louisiana, or Challenge Grant program, to support 12 innovative projects that attempted to address the root causes of obesity through integrated changes in policies, norms, practices, social supports, and the physical environment. The BCBSLAF partnered with the Pennington Biomedical Research Center to develop the structure of the program, provide scientific oversight, and coordinate evaluation activities.

The Challenge for a Healthier Louisiana Grant Program

The Challenge Grant program funded 12 community partnerships around the state of Louisiana. These partnerships facilitated the development and implementation of community-based obesity prevention by promoting healthier eating and/or PA through enhanced infrastructure, policy changes, and programming. The Challenge Grant program was grounded in the socioecological models for HE (Story, Kaphingst, Robinson-O’Brien, & Glanz, 2008) and PA (Sallis et al., 2006). Applicants were required to develop interventions that targeted multiple levels of influence (i.e., individual, family, community, etc.) in their projects, and they were encouraged to adopt strategies to prevent obesity recommended by the U.S. Centers for Disease Control and Prevention (CDC, 2009).

The approaches used by grantees were community-tailored and included a variety of approaches, including PA and HE components in addition to broader media and advocacy/policy approaches (Table 1). Most projects included components targeting both PA and HE; however, two projects chose to focus efforts on environmental changes related to only one behavior. Intervention components targeted multiple levels of influence, and activities included HE and PA education (classes); construction and improvement of trails, playgrounds, ball courts, and splash pads; new and expanded farmers markets and market pavilions; corner store and park improvements; healthier menus/items in restaurants, vending, and meal programs; advocacy/policy; and media communications. One project focused exclusively on children, and the results of this project are not included in this analysis.

Table 1

Components of the Challenge Grant Program Projects

Project	Physical Activity			Healthy Eating				Media	Advocacy/Policy
Project	Trails	Playgrounds, Exercise Stations	Parks: Facilities, Programming, Improvements	Farmer’s Markets	Grocery/Corner Store Improvements	Incentive Program	Community/School Gardens	Media	Advocacy/Policy
1		×		×	×	×		×	×
2				×	×	×	×		×
3	×	×	×	×	×		×	×	×
4	×	×	×		×		×	×	×
5		×		×	×	×	×	×	×
6	×	×	×					×	×
7			×	×	×		×	×	×
8	×	×	×	×			×	×
9	×	×	×	×	×	×	×	×	×
10^a		×	×			×	×	×	×
11		×	×	×		×		×	×
12			×	×		×	×	×

Project 10 was focused on children only and was excluded from the statistical analysis reported in this article.

Grantee communities were required to match the BCBSLAF financial contribution. This match was important to demonstrate the community’s commitment and support of the work and to increase the impact of the program. Overall, the Challenge Grant program included an investment of $10.2 million from the BCBSLAF and $16.8 million of community match funding.

Evaluation of Changes in Targeted Behaviors

To evaluate whether the Challenge Grant program resulted in changes in HE and/or PA among adults, surveys were conducted at two time points, roughly beginning-to-middle of Year 2 (pre) and middle-to-end of Year 3 (post). Although the evaluation design did not include surveys from control communities, certain projects involved activities targeting only HE or PA; therefore, these projects were able to act as pseudo control communities for behavior-specific analyses. Also, survey questions queried participant participation in various program components; therefore, participants reporting participation in components targeting one behavior but not the other (i.e., HE vs. PA) were able to serve as a pseudo control group for behavior-specific analyses.

Survey questions on the behavioral outcomes (HE and PA) were obtained from the Behavioral Risk Factor Surveillance System (U.S. Centers for Disease Control and Prevention, 2018a) in order to facilitate comparisons to the general Louisiana population. Self-reported height and weight were also collected in order to evaluate changes in BMI (kg/m²). HE was measured via responses to a question assessing how frequently the respondent ate fruits and vegetables. These responses were summarized as a “times per day” estimate and categorized as “at least one fruit per day” and/or “at least one vegetable per day” (yes vs. no). PA was assessed via respondents’ providing the names, frequencies, and durations of the two most frequent types of PA or exercise in which he/she engaged in the past month. Similar to BRFSS, these activities were coded according to standard MET values from the 2011 Compendium of Physical Activities (Ainsworth et al., 2011). Responses were summarized as “minutes of moderate or vigorous activity per week” and categorized as “meets 2008 aerobic guidelines” (yes vs. no) according to these values.

A total of 3,860 adult participant surveys (2,853 at baseline; 1,007 at follow-up) were collected from 11 of the Challenge Grant projects. Of these, 866 surveys were completed at both time points and with sufficient data to be included in the analysis, that is, participant provided a response to at least one behavioral outcome, responded to survey questions assessing program participation, and provided information on key demographic items. All procedures were approved by the Pennington Biomedical Research Center Institutional Review Board, and informed consent was obtained from all participants prior to participation.

Statistical Analysis

The relationship between status at follow-up and exposure to a Challenge Grant program that promoted HE or PA was assessed using logistic regression in models that included baseline status, program participation, site, and propensity to participate in the HE/PA program (propensity score quintile, details below) as covariates. Two-way interactions between baseline, site, and/or propensity score quintile were assessed when main effects were significant at p < .05; interactions were retained when p < .2. Least-square mean estimates at follow-up for persons participating versus not participating, by baseline status, were estimated in separate models that included a participation-by-baseline interaction, regardless of the significance of the interaction.

Propensity Scores

Propensity score adjustment is recommended for analysis in situations like the Challenge Grant evaluation, in which individuals are not randomized into treatment conditions, in this case whether an individual was exposed to a HE/PA program or not. During propensity score adjustment, a person’s “propensity” to be exposed is first modeled from available data using logistic regression. Hence, a person’s propensity score is the predicted probability of participating, based on his or her characteristics. Two individuals with a similar propensity score are considered more-or-less equivalent in their underlying chance of being exposed to the program, so adjustment for propensity scores can create a situation analogous to randomization, such that any differences in the outcomes (between the one who participated and the other who didn’t) can be better attributed to the program. For our analyses, separate propensity scores were created for (a) HE program participation and (b) PA program participation and categorized into propensity score quintiles. Propensity score models showed adequate fit (Hosmer–Lemeshow goodness-of-fit p > .3). Figure 1 shows adequate overlap in propensity scores, yet differential propensities to participate. Consequently, using propensity scores to account for these differences in our analyses was warranted.

Figure 1

Propensity Scores for (A) Healthy Eating Program Participation and (B) Physical Activity Program Participation According to Actual Program Participation. Propensity score quintiles rank the propensity for program participation

Results

A catalog of the infrastructure developed and the activities associated with the Challenge Grant program was compiled through direct communication with the grantees. Overall, there was a strong emphasis among the projects on developing infrastructure in communities to support PA and HE. Over the course of the program, 107 new community, school, and home gardens were developed, 34 new or improved sidewalks and walking trails were constructed, 78 new or improved farmers markets were facilitated, 49 parks, schools and health centers were improved, 25 miles of walking/biking paths were constructed, and 577,464 pounds of fresh produce were distributed.

Table 2 provides baseline descriptive characteristics of adult Challenge Grant participants who provided survey responses at baseline and at follow-up. Overall, the sample of Challenge Grant participants was similar to the general Louisiana population with respect to several key demographic variables. However, the Challenge Grant participants were more likely to be food insecure, female, and African American. These results are expected, as the Challenge Grant projects were directed in many cases toward underserved, minority populations rather than the general population of Louisiana.

Table 2

Demographics and Selected Baseline Behavioral Characteristics of Adult Challenge Grant Program Participants, With Comparison to the Louisiana Adult Population

	Adult Challenge Grant Participants	Louisiana
Total (N)	866	3,515,357
Age (%)^a
18-39 years	42.7	40.4
40-60 years	33.1	34.7
>60 years	24.1	24.9
Sex (%)^a
Female	78.2	51.7
Race (%)^a
African American	45.6	32.3
White	44.9	62.9
Other	9.5	4.8
Highest education (%)^a
High school or less	26.3	47.2
Some college/college degree	50.0	45.2
Some graduate work/advanced degree	23.7	7.6
Employment status (%)^a
Full-time	44.8	—
Part-time	15.2	—
Retired	20.8	—
Unemployed	19.2	8.1
Food insecurity (%)^b	40.0	25.7
Health indicators (%)^c
Eats fruit at least once per day	60.2	46.5
Eats vegetables at least once per day	72.4	67.3
Participated in physical activity or exercise during the past month (outside of regular job)	73.2	67.8
Meets guidelines for aerobic physical activity	47.2	45.1
Obese (BMI ⩾ 30 kg/m²)	36.9	33.1

NOTE: BMI = body mass index.

Source of data for Louisiana is the U.S. Census (estimates for 2013; United States Census Bureau, 2017). ^bSource of data for Louisiana is the 2013 Behavioral Risk Factor Surveillance System as reported in Njai, Siegel, Yin, and Liao, 2017. ^cSource of data for Louisiana is the 2013 Behavioral Risk Factor Surveillance System (Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, & Division of Nutrition Physical Activity and Obesity, 2018).

Participation in HE programs ranged from approximately 50% to 93%, whereas participation in PA components ranged from 16% to 94%. Among sites with both PA and HE programs, the percentage of participants who reported participating in both programs ranged from 23% to 89%. The average time between the baseline and follow-up surveys ranged from approximately 12 months to 19 months, with an overall average across all sites of 13.9 months. Among participants reporting height and weight at both time points (n = 653), the BMI (mean ± standard deviation) was 29.4 ± 8.0 kg/m² at baseline and 29.3 ± 7.8 kg/m² at follow-up.

The results of the logistic regression analyses are presented in Table 3. Participants who were exposed to PA programs were more likely to adopt the consumption of fruits (odds ratio = 2.0; 95% confidence interval [1.1, 3.6]), were more likely to eat vegetables once per day (p = .028), and were more likely to participate in PA (p = .053). Participants who were exposed to HE programs were more likely to eat fruit once per day (p = .035), were more likely to eat vegetables at least once a day (p = .008), and were more likely to participate in PA (p = .018).

Table 3

Health Behaviors at Follow-up, by Baseline Characteristics and Challenge Grant Program Component Exposure

	Eats Fruit at Least Once per Day		Eats Vegetables at Least Once per Day		Participated in Physical Activity or Exercise During the Past Month (Outside of Regular Job)		Meets Guidelines for Aerobic Physical Activity
	Adoption^a	Maintenance^b	Adoption	Maintenance	Adoption	Maintenance	Adoption	Maintenance
Exposure to physical activity component
Yes (%)	55.4	80.7	51.4	98.3	54.0	88.6	35.5	58.7
No (%; reference)	38.8	76.5	43.8	96.5	32.8	85.9	25.5	60.8
Odds ratio	2.0 (1.1, 3.6)	1.3 (0.7, 2.3)	1.4 (0.6, 3.1)	2.2 (1.1, 4.2)	2.4 (1.1, 5.3)	1.3 (0.6, 2.6)	1.6 (0.9. 3.0)	0.9 (0.5, 1.7)
p value^c	.0661		.0278		.0529		.4654
Exposure to healthy eating component
Yes (%)	50.5	79.7	58.6	83.6	58.6	86.4	34.6	61.0
No (%; reference)	43.2	69.9	42.7	76.2	36.3	82.3	31.9	56.3
Odds ratio	1.3 (0.8, 2.1)	1.7 (1.0, 2.8)	1.9 (1.1, 3.4)	1.6 (1.0, 2.6)	2.5 (1.3, 5.0)	1.4 (0.8, 2.4)	1.1 (0.7, 1.9)	1.2 (0.7, 2.1)
p value^c	.0349		.0079		.0178		.4822

NOTE: Odds ratios in bold are statistically significant (p < .05). LS mean estimates; model adjusts for program participation, baseline behavior, program participation × baseline, site, site × baseline (where warranted), and propensity for program participation.

Persons not reporting behavior at baseline. ^bPersons who report engaging in behavior at baseline. ^cp value corresponding to the pooled effect of program participation across both behavior adopters and maintainers.

Discussion

The programs funded under the Challenge Grant initiative did not take a clinical approach to the problem of obesity, rather they employed a number of CDC’s recommended strategies for community-based obesity prevention (CDC, 2009). For example, the grantees used strategies to (a) promote the availability of affordable healthy food and beverages, (b) support healthy food and beverage choices, and (c) create safe communities that support PA. Furthermore, the overarching goal of the Challenge Grant program was to encourage communities to organize for change, which is another recommended strategy from CDC (2009). Overall, the strategies employed by the Challenge Grant program were evidence based, grounded in best practices, and were consistent with Policy Systems and Environmental change approaches (Honeycutt et al., 2015).

The results of this study indicate that the Challenge Grant program resulted in positive changes in weight-related health behaviors among participants. These results support the contention that community-based obesity prevention and health promotion efforts are feasible and may influence health behaviors. No changes in BMI were observed among Challenge Grant participants; however, there were indications for improvements in HE and PA. Given the difficulty in achieving measurable changes in obesity and obesity-related behaviors using community-based approaches (Baker, Francis, Soares, Weightman, & Foster, 2015; Baranowski & Lytle, 2015; Reeder & Katzmarzyk, 2007), we are encouraged by these results. It is likely that the interventions were not intensive enough to result in significant disruptions in energy balance required to change body weight in the short-term. Similar results were obtained in the Healthy Alberta Communities Project, where environmental interventions aimed at obesity and chronic disease prevention implemented over 3 years failed to produce measurable effects in the intervention communities compared with population surveillance data of BMI and obesity-related health behaviors (Raine et al., 2013). However, these results are consistent with the “small changes” approach to obesity prevention, whereby small, positive changes in health-related behaviors are believed to be important in sustaining long-term energy balance (Hills, Byrne, Lindstrom, & Hill, 2013). As with many public health outcomes, the benefits of community-level efforts to reduce obesity may not be evident for many years.

The evaluation of community-based obesity prevention programs is a challenging prospect from several perspectives. First, it is difficult to measure changes in energy balance-related behaviors at the population level—that is, we expect these programs to result in small changes but are we able to detect these changes using program evaluation methods? A related limitation is that the questionnaires employed in this study, based on the Behavioral Risk Factor Surveillance System, are designed to monitor population-level changes rather than individual-level changes; although the use of these questionnaires was beneficial to be able to compare results to the Louisiana population, their lack of sensitivity was a limitation, as cautioned by the CDC (2018b). Second, given the nature of and variety of the interventions employed in the Challenge Grant program, it was difficult to carefully control for confounding factors that may have influenced the outcomes. Furthermore, the timing of the pre- and postsurvey administration was not well standardized. By necessity, in many cases the baseline surveys were administered to participants after they had been exposed to some elements of the intervention, as this is how the participants were identified. We were limited to evaluating changes among adult participants who reported participation in the funded projects, and we were unable to evaluate changes in lifestyle behaviors in the broader community or incorporate a true control group, which is a limitation of our design. As R. A. Goodman, Bunnell, and Posner (2014) note, the complexity of community program evaluation efforts dictates that “clean” scientific methods and traditional experimental designs are not applicable in many circumstances. Indeed, detecting significant effects of these programs is hampered by methodological limitations related to study design and concurrent secular trends, among other factors (Merzel & D’Afflitti, 2003). In this context, our use of propensity scores is an innovative method that can be used to advance this field.

Conclusions

The Challenge for a Healthier Louisiana program mobilized communities across the state of Louisiana to improve PA and HE through enhanced infrastructure, policy changes, and programming. There is some indication of changes in health behaviors among program participants; however, the sustainability of these changes over the long term will require further evaluation.

Implications for Practice

The results support a need for funders to invest in community-level initiatives that combine infrastructure improvements, policy changes, and programming, particularly in high-need, low-resource areas. While this study was successful in demonstrating positive changes in obesity-related behaviors across a variety of interventions and settings, the limitations of the study design suggest a need for innovative methods and tools that are flexible and feasible to implement in the community setting. Community-based evaluation designs should aim to balance community capacity, cultural relevance, and scientific rigor. Finally, a community-based project relies on strong partnerships. As with any partnership, it is vital to work with people/organizations with appropriate levels of commitment, as well as those with shared values/mission.

Footnotes

Authors’ Note:

Elizabeth A. Gollub is now at the School of Nutrition and Food Sciences, LSU AgCenter in Baton Rouge, LA. Allison Tohme is now a Farmers Market Program Developer at Central Louisiana Economic Development Alliance in Alexandria, LA. The authors would like to thank Christy Reeves and Lydia Martin who were with the Blue Cross and Blue Shield of Louisiana Foundation for the majority of the project and Michael Tipton who closed out the grant on behalf of the Foundation, and Denese Vlosky who was at the Pennington Biomedical Research Center at the time this work was done. This work was funded by the Blue Cross and Blue Shield of Louisiana Foundation and the authors would like to thank the Foundation Board and its various board members who participated in meetings and provided insights throughout the grant period.

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